From e85e2ee12720c9c8c053267a193c8bdc1424ccc8 Mon Sep 17 00:00:00 2001 From: Lunkhound Date: Mon, 5 Jun 2017 14:43:41 -0700 Subject: [PATCH 01/10] MultiThreadedDemo: make it possible to dial in a single large island, and tilt the ground and adjust rolling friction --- .../MultiThreadedDemo/MultiThreadedDemo.cpp | 135 ++++++++++++++---- 1 file changed, 105 insertions(+), 30 deletions(-) diff --git a/examples/MultiThreadedDemo/MultiThreadedDemo.cpp b/examples/MultiThreadedDemo/MultiThreadedDemo.cpp index a083b9ac5..a04ab0d91 100644 --- a/examples/MultiThreadedDemo/MultiThreadedDemo.cpp +++ b/examples/MultiThreadedDemo/MultiThreadedDemo.cpp @@ -13,28 +13,35 @@ subject to the following restrictions: 3. This notice may not be removed or altered from any source distribution. */ -#include "btBulletDynamicsCommon.h" -#include "LinearMath/btQuickprof.h" -#include "LinearMath/btIDebugDraw.h" +#include "MultiThreadedDemo.h" +#include "CommonRigidBodyMTBase.h" #include "../CommonInterfaces/CommonParameterInterface.h" +#include "btBulletDynamicsCommon.h" +#include "btBulletCollisionCommon.h" +#include "LinearMath/btAlignedObjectArray.h" + #include //printf debugging #include -class btCollisionShape; - -#include "CommonRigidBodyMTBase.h" -#include "MultiThreadedDemo.h" -#include "LinearMath/btAlignedObjectArray.h" -#include "btBulletCollisionCommon.h" - static btScalar gSliderStackRows = 8.0f; static btScalar gSliderStackColumns = 6.0f; static btScalar gSliderStackHeight = 10.0f; +static btScalar gSliderStackWidth = 1.0f; static btScalar gSliderGroundHorizontalAmplitude = 0.0f; static btScalar gSliderGroundVerticalAmplitude = 0.0f; +static btScalar gSliderGroundTilt = 0.0f; +static btScalar gSliderRollingFriction = 0.0f; +static bool gSpheresNotBoxes = false; +static void boolPtrButtonCallback( int buttonId, bool buttonState, void* userPointer ) +{ + if ( bool* val = static_cast( userPointer ) ) + { + *val = !*val; + } +} /// MultiThreadedDemo shows how to setup and use multithreading class MultiThreadedDemo : public CommonRigidBodyMTBase @@ -50,8 +57,9 @@ class MultiThreadedDemo : public CommonRigidBodyMTBase btRigidBody* m_groundBody; btTransform m_groundStartXf; float m_groundMovePhase; + float m_prevRollingFriction; - void createStack( const btVector3& pos, btCollisionShape* boxShape, const btVector3& halfBoxSize, int size ); + void createStack( const btTransform& trans, btCollisionShape* boxShape, const btVector3& halfBoxSize, int size, int width ); void createSceneObjects(); void destroySceneObjects(); @@ -62,10 +70,25 @@ public: virtual ~MultiThreadedDemo() {} + btQuaternion getGroundRotation() const + { + btScalar tilt = gSliderGroundTilt * SIMD_2_PI / 360.0f; + return btQuaternion( btVector3( 1.0f, 0.0f, 0.0f ), tilt ); + } virtual void stepSimulation( float deltaTime ) BT_OVERRIDE { if ( m_dynamicsWorld ) { + if ( m_prevRollingFriction != gSliderRollingFriction ) + { + m_prevRollingFriction = gSliderRollingFriction; + btCollisionObjectArray& objArray = m_dynamicsWorld->getCollisionObjectArray(); + for ( int i = 0; i < objArray.size(); ++i ) + { + btCollisionObject* obj = objArray[ i ]; + obj->setRollingFriction( gSliderRollingFriction ); + } + } if (m_groundBody) { // update ground @@ -85,6 +108,7 @@ public: offset[kUpAxis] = gndVOffset; vel[kUpAxis] = gndVVel; xf.setOrigin(xf.getOrigin() + offset); + xf.setRotation( getGroundRotation() ); m_groundBody->setWorldTransform( xf ); m_groundBody->setLinearVelocity( vel ); } @@ -117,6 +141,7 @@ MultiThreadedDemo::MultiThreadedDemo(struct GUIHelperInterface* helper) m_cameraPitch = -30.0f; m_cameraYaw = 90.0f; m_cameraDist = 48.0f; + m_prevRollingFriction = -1.0f; helper->setUpAxis( kUpAxis ); } @@ -134,6 +159,13 @@ void MultiThreadedDemo::initPhysics() slider.m_clampToIntegers = true; m_guiHelper->getParameterInterface()->registerSliderFloatParameter( slider ); } + { + SliderParams slider( "Stack width", &gSliderStackWidth ); + slider.m_minVal = 1.0f; + slider.m_maxVal = 30.0f; + slider.m_clampToIntegers = true; + m_guiHelper->getParameterInterface()->registerSliderFloatParameter( slider ); + } { SliderParams slider( "Stack rows", &gSliderStackRows ); slider.m_minVal = 1.0f; @@ -164,7 +196,30 @@ void MultiThreadedDemo::initPhysics() slider.m_clampToNotches = false; m_guiHelper->getParameterInterface()->registerSliderFloatParameter( slider ); } - + { + // ground tilt + SliderParams slider( "Ground tilt", &gSliderGroundTilt ); + slider.m_minVal = -45.0f; + slider.m_maxVal = 45.0f; + slider.m_clampToNotches = false; + m_guiHelper->getParameterInterface()->registerSliderFloatParameter( slider ); + } + { + // rolling friction + SliderParams slider( "Rolling friction", &gSliderRollingFriction ); + slider.m_minVal = 0.0f; + slider.m_maxVal = 1.0f; + slider.m_clampToNotches = false; + m_guiHelper->getParameterInterface()->registerSliderFloatParameter( slider ); + } + { + ButtonParams button( "Spheres not boxes", 0, false ); + button.m_initialState = gSpheresNotBoxes; + button.m_userPointer = &gSpheresNotBoxes; + button.m_callback = boolPtrButtonCallback; + m_guiHelper->getParameterInterface()->registerButtonParameter( button ); + } + createSceneObjects(); m_guiHelper->createPhysicsDebugDrawer( m_dynamicsWorld ); @@ -184,29 +239,36 @@ btRigidBody* MultiThreadedDemo::localCreateRigidBody(btScalar mass, const btTran } -void MultiThreadedDemo::createStack( const btVector3& center, btCollisionShape* boxShape, const btVector3& halfBoxSize, int size ) +void MultiThreadedDemo::createStack( const btTransform& parentTrans, btCollisionShape* boxShape, const btVector3& halfBoxSize, int height, int width ) { btTransform trans; trans.setIdentity(); + trans.setRotation( parentTrans.getRotation() ); float halfBoxHeight = halfBoxSize.y(); float halfBoxWidth = halfBoxSize.x(); - for ( int i = 0; isetFriction(1.0f); + btRigidBody* body = localCreateRigidBody( mass, trans, boxShape ); + body->setFriction( 1.0f ); + body->setRollingFriction( gSliderRollingFriction ); + } } } } @@ -216,10 +278,8 @@ void MultiThreadedDemo::createSceneObjects() { { // create ground box - btTransform tr; - tr.setIdentity(); - tr.setOrigin( btVector3( 0.f, -3.f, 0.f ) ); - m_groundStartXf = tr; + m_groundStartXf.setOrigin( btVector3( 0.f, -3.f, 0.f ) ); + m_groundStartXf.setRotation( getGroundRotation() ); //either use heightfield or triangle mesh @@ -240,18 +300,33 @@ void MultiThreadedDemo::createSceneObjects() int numStackRows = btMax(1, int(gSliderStackRows)); int numStackCols = btMax(1, int(gSliderStackColumns)); int stackHeight = int(gSliderStackHeight); - float stackZSpacing = 3.0f; + int stackWidth = int( gSliderStackWidth ); + float stackZSpacing = 2.0f + stackWidth*halfExtents.x()*2.0f; float stackXSpacing = 20.0f; btBoxShape* boxShape = new btBoxShape( halfExtents ); m_collisionShapes.push_back( boxShape ); + btSphereShape* sphereShape = new btSphereShape( 0.5f ); + m_collisionShapes.push_back( sphereShape ); + + btCollisionShape* shape = boxShape; + if ( gSpheresNotBoxes ) + { + shape = sphereShape; + } + + btTransform groundTrans; + groundTrans.setIdentity(); + groundTrans.setRotation( getGroundRotation() ); for ( int iX = 0; iX < numStackCols; ++iX ) { for ( int iZ = 0; iZ < numStackRows; ++iZ ) { btVector3 center = btVector3( iX * stackXSpacing, 0.0f, ( iZ - numStackRows / 2 ) * stackZSpacing ); - createStack( center, boxShape, halfExtents, stackHeight ); + btTransform trans = groundTrans; + trans.setOrigin( groundTrans( center ) ); + createStack( trans, shape, halfExtents, stackHeight, stackWidth ); } } } From b7f35000aad379a73c9eb628c86dbaf7cbf532e1 Mon Sep 17 00:00:00 2001 From: Lunkhound Date: Sun, 2 Jul 2017 17:28:07 -0700 Subject: [PATCH 02/10] NNCG solver: apply rolling friction consistently regardless of the solver flags --- .../btNNCGConstraintSolver.cpp | 115 ++---------------- 1 file changed, 13 insertions(+), 102 deletions(-) diff --git a/src/BulletDynamics/ConstraintSolver/btNNCGConstraintSolver.cpp b/src/BulletDynamics/ConstraintSolver/btNNCGConstraintSolver.cpp index f110cd480..f3979be35 100644 --- a/src/BulletDynamics/ConstraintSolver/btNNCGConstraintSolver.cpp +++ b/src/BulletDynamics/ConstraintSolver/btNNCGConstraintSolver.cpp @@ -78,20 +78,6 @@ btScalar btNNCGConstraintSolver::solveSingleIteration(int iteration, btCollision btScalar deltaflengthsqr = 0; - - if (infoGlobal.m_solverMode & SOLVER_SIMD) - { - for (int j=0;jisEnabled()) - { - int bodyAid = getOrInitSolverBody(constraints[j]->getRigidBodyA(),infoGlobal.m_timeStep); - int bodyBid = getOrInitSolverBody(constraints[j]->getRigidBodyB(),infoGlobal.m_timeStep); - btSolverBody& bodyA = m_tmpSolverBodyPool[bodyAid]; - btSolverBody& bodyB = m_tmpSolverBodyPool[bodyBid]; - constraints[j]->solveConstraintObsolete(bodyA,bodyB,infoGlobal.m_timeStep); - } - } - ///solve all contact constraints - int numPoolConstraints = m_tmpSolverContactConstraintPool.size(); - for (int j=0;jbtScalar(0)) - { - solveManifold.m_lowerLimit = -(solveManifold.m_friction*totalImpulse); - solveManifold.m_upperLimit = solveManifold.m_friction*totalImpulse; - - btScalar deltaf = resolveSingleConstraintRowGeneric(m_tmpSolverBodyPool[solveManifold.m_solverBodyIdA],m_tmpSolverBodyPool[solveManifold.m_solverBodyIdB],solveManifold); - m_deltafCF[j] = deltaf; - deltaflengthsqr += deltaf*deltaf; - } else { - m_deltafCF[j] = 0; - } - } - - int numRollingFrictionPoolConstraints = m_tmpSolverContactRollingFrictionConstraintPool.size(); - for (int j=0;jbtScalar(0)) - { - btScalar rollingFrictionMagnitude = rollingFrictionConstraint.m_friction*totalImpulse; - if (rollingFrictionMagnitude>rollingFrictionConstraint.m_friction) - rollingFrictionMagnitude = rollingFrictionConstraint.m_friction; - - rollingFrictionConstraint.m_lowerLimit = -rollingFrictionMagnitude; - rollingFrictionConstraint.m_upperLimit = rollingFrictionMagnitude; - - btScalar deltaf = resolveSingleConstraintRowGeneric(m_tmpSolverBodyPool[rollingFrictionConstraint.m_solverBodyIdA],m_tmpSolverBodyPool[rollingFrictionConstraint.m_solverBodyIdB],rollingFrictionConstraint); - m_deltafCRF[j] = deltaf; - deltaflengthsqr += deltaf*deltaf; - } else { - m_deltafCRF[j] = 0; - } - } - } } @@ -362,10 +278,7 @@ btScalar btNNCGConstraintSolver::solveSingleIteration(int iteration, btCollision for (int j=0;j Date: Fri, 14 Jul 2017 23:38:15 +0200 Subject: [PATCH 03/10] Add nearly all gym environments using pybullet together with the latest tf model from the roboschool model zoo. --- .../gym/enjoy_TF_PybulletAnt_v0_2017may.py | 292 + ...enjoy_TF_PybulletHalfCheetah_v0_2017may.py | 290 + .../gym/enjoy_TF_PybulletHopper_v0_2017may.py | 279 + .../enjoy_TF_PybulletHumanoid_v0_2017may.py | 501 + ...bulletInvertedDoublePendulum_v0_2017may.py | 178 + ...ulletInvertedPendulumSwingup_v0_2017may.py | 174 + ..._TF_PybulletInvertedPendulum_v0_2017may.py | 173 + .../enjoy_TF_PybulletWalker2d_v0_2017may.py | 286 + .../pybullet/gym/envs/gym_forward_walkers.py | 233 + .../pybullet/gym/envs/gym_manipulators.py | 304 + .../pybullet/gym/envs/gym_mujoco_xml_env.py | 246 + examples/pybullet/gym/envs/gym_pendula.py | 133 + examples/pybullet/gym/envs/kerasrl_utils.py | 28 + .../pybullet/gym/envs/mujoco_assets/ant.xml | 71 + .../gym/envs/mujoco_assets/ground_plane.xml | 5 + .../gym/envs/mujoco_assets/half_cheetah.xml | 88 + .../gym/envs/mujoco_assets/hopper.xml | 39 + .../envs/mujoco_assets/humanoid_symmetric.xml | 107 + .../humanoid_symmetric_no_ground.xml | 115 + .../inverted_double_pendulum.xml | 47 + .../envs/mujoco_assets/inverted_pendulum.xml | 27 + .../gym/envs/mujoco_assets/pusher.xml | 91 + .../gym/envs/mujoco_assets/reacher.xml | 39 + .../gym/envs/mujoco_assets/striker.xml | 101 + .../gym/envs/mujoco_assets/thrower.xml | 127 + .../gym/envs/mujoco_assets/walker2d.xml | 52 + .../models_outdoor/stadium/part0.obj | 23154 ++++++++++++ .../models_outdoor/stadium/part1.obj | 16210 ++++++++ .../models_outdoor/stadium/part2.obj | 8350 +++++ .../models_outdoor/stadium/stadium.mtl | 16 + .../models_outdoor/stadium/stadium.obj | 30561 ++++++++++++++++ .../models_outdoor/stadium/stadium_grass.jpg | Bin 0 -> 1054660 bytes .../gym/envs/other_assets/stadium.sdf | 107 + examples/pybullet/gym/envs/scene_abstract.py | 84 + examples/pybullet/gym/envs/scene_stadium.py | 28 + 35 files changed, 82536 insertions(+) create mode 100644 examples/pybullet/gym/enjoy_TF_PybulletAnt_v0_2017may.py create mode 100644 examples/pybullet/gym/enjoy_TF_PybulletHalfCheetah_v0_2017may.py create mode 100644 examples/pybullet/gym/enjoy_TF_PybulletHopper_v0_2017may.py create mode 100644 examples/pybullet/gym/enjoy_TF_PybulletHumanoid_v0_2017may.py create mode 100644 examples/pybullet/gym/enjoy_TF_PybulletInvertedDoublePendulum_v0_2017may.py create mode 100644 examples/pybullet/gym/enjoy_TF_PybulletInvertedPendulumSwingup_v0_2017may.py create mode 100644 examples/pybullet/gym/enjoy_TF_PybulletInvertedPendulum_v0_2017may.py create mode 100644 examples/pybullet/gym/enjoy_TF_PybulletWalker2d_v0_2017may.py create mode 100644 examples/pybullet/gym/envs/gym_forward_walkers.py create mode 100644 examples/pybullet/gym/envs/gym_manipulators.py create mode 100644 examples/pybullet/gym/envs/gym_mujoco_xml_env.py create mode 100644 examples/pybullet/gym/envs/gym_pendula.py create mode 100644 examples/pybullet/gym/envs/kerasrl_utils.py create mode 100644 examples/pybullet/gym/envs/mujoco_assets/ant.xml create mode 100644 examples/pybullet/gym/envs/mujoco_assets/ground_plane.xml create mode 100644 examples/pybullet/gym/envs/mujoco_assets/half_cheetah.xml create mode 100644 examples/pybullet/gym/envs/mujoco_assets/hopper.xml create mode 100644 examples/pybullet/gym/envs/mujoco_assets/humanoid_symmetric.xml create mode 100644 examples/pybullet/gym/envs/mujoco_assets/humanoid_symmetric_no_ground.xml create mode 100644 examples/pybullet/gym/envs/mujoco_assets/inverted_double_pendulum.xml create mode 100644 examples/pybullet/gym/envs/mujoco_assets/inverted_pendulum.xml create mode 100644 examples/pybullet/gym/envs/mujoco_assets/pusher.xml create mode 100644 examples/pybullet/gym/envs/mujoco_assets/reacher.xml create mode 100644 examples/pybullet/gym/envs/mujoco_assets/striker.xml create mode 100644 examples/pybullet/gym/envs/mujoco_assets/thrower.xml create mode 100644 examples/pybullet/gym/envs/mujoco_assets/walker2d.xml create mode 100644 examples/pybullet/gym/envs/other_assets/roboschool/models_outdoor/stadium/part0.obj create mode 100644 examples/pybullet/gym/envs/other_assets/roboschool/models_outdoor/stadium/part1.obj create mode 100644 examples/pybullet/gym/envs/other_assets/roboschool/models_outdoor/stadium/part2.obj create mode 100644 examples/pybullet/gym/envs/other_assets/roboschool/models_outdoor/stadium/stadium.mtl create mode 100644 examples/pybullet/gym/envs/other_assets/roboschool/models_outdoor/stadium/stadium.obj create mode 100644 examples/pybullet/gym/envs/other_assets/roboschool/models_outdoor/stadium/stadium_grass.jpg create mode 100644 examples/pybullet/gym/envs/other_assets/stadium.sdf create mode 100644 examples/pybullet/gym/envs/scene_abstract.py create mode 100644 examples/pybullet/gym/envs/scene_stadium.py diff --git a/examples/pybullet/gym/enjoy_TF_PybulletAnt_v0_2017may.py b/examples/pybullet/gym/enjoy_TF_PybulletAnt_v0_2017may.py new file mode 100644 index 000000000..ab6e99364 --- /dev/null +++ b/examples/pybullet/gym/enjoy_TF_PybulletAnt_v0_2017may.py @@ -0,0 +1,292 @@ +import gym +import numpy as np +import pybullet as p +import pybulletgym.envs +import time + +def relu(x): + return np.maximum(x, 0) + +class SmallReactivePolicy: + "Simple multi-layer perceptron policy, no internal state" + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 128) + assert weights_dense2_w.shape == (128, 64) + assert weights_final_w.shape == (64, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x + +def demo_run(): + env = gym.make("PybulletAnt-v0") + + cid = p.connect(p.SHARED_MEMORY) # only show graphics if the browser is already running.... + if cid < 0: + cid = p.connect(p.DIRECT) + + pi = SmallReactivePolicy(env.observation_space, env.action_space) + + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() + + while 1: + time.sleep(0.01) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + still_open = env.render("human") + if still_open==False: + return + if not done: continue + if restart_delay==0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60*2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay==0: break + +weights_dense1_w = np.array([ +[ +0.2856, -0.4091, +0.3380, -0.1083, +0.1049, -0.0806, -0.0827, +0.0402, +0.2415, +0.0927, +0.1812, +0.5455, +0.3157, +0.1043, +0.0527, +0.3768, -0.2083, +0.1218, -0.0633, +0.3236, +0.2198, +0.0513, +0.3505, -0.1165, -0.1732, +0.2760, +0.0006, +0.0249, +0.5887, -0.2324, +0.1503, -0.0108, +0.1220, +0.2527, +0.0984, +0.5360, -0.0579, -0.0847, +0.1649, +0.0589, -0.2046, +0.5981, -0.1063, -0.1088, +0.3424, +0.3479, -0.1751, +0.2924, -0.0610, +0.0807, +0.3291, +0.0059, +0.0339, -0.2823, +0.2533, +0.3156, +0.1679, +0.2417, +0.1265, +0.0024, +0.0802, +0.2531, -0.2576, +0.3894, +0.3206, +0.2015, +0.3482, +0.1042, -0.2418, -0.0002, +0.1277, +0.1811, +0.1551, +0.5460, +0.1714, +0.1021, -0.1252, +0.0609, -0.0372, -0.0351, +0.1216, +0.0023, -0.2448, +0.0077, +0.0584, +0.2389, -0.0848, +0.3542, 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-0.0680, +0.1401, -0.0628, -0.1317, +0.1489, +0.1844, -0.1147, +0.0137]) + +if __name__=="__main__": + demo_run() diff --git a/examples/pybullet/gym/enjoy_TF_PybulletHalfCheetah_v0_2017may.py b/examples/pybullet/gym/enjoy_TF_PybulletHalfCheetah_v0_2017may.py new file mode 100644 index 000000000..5446a7ec9 --- /dev/null +++ b/examples/pybullet/gym/enjoy_TF_PybulletHalfCheetah_v0_2017may.py @@ -0,0 +1,290 @@ +import gym +import numpy as np +import pybullet as p +import pybulletgym.envs +import time + +def relu(x): + return np.maximum(x, 0) + +class SmallReactivePolicy: + "Simple multi-layer perceptron policy, no internal state" + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 128) + assert weights_dense2_w.shape == (128, 64) + assert weights_final_w.shape == (64, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x + +def demo_run(): + env = gym.make("PybulletHalfCheetah-v0") + + cid = p.connect(p.SHARED_MEMORY) # only show graphics if the browser is already running.... + if cid < 0: + cid = p.connect(p.DIRECT) + + pi = SmallReactivePolicy(env.observation_space, env.action_space) + + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() + + while 1: + time.sleep(0.01) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + still_open = env.render("human") + if still_open==False: + return + if not done: continue + if restart_delay==0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60*2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay==0: break + +weights_dense1_w = np.array([ +[ -0.4105, +0.5540, +0.2791, +0.4953, -0.0309, +0.8480, +0.1630, +0.0630, +0.7107, +0.0597, +0.2675, -0.1931, -0.2436, +0.2909, +0.3563, +0.4564, -0.0445, -0.2228, 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-0.2151], +[ -0.5111, +0.2231, -0.1034, +0.0479, -0.0543, +0.0605], +[ +0.0822, -0.4530, -0.0345, +0.1621, -0.2487, +0.1516] +]) + +weights_final_b = np.array([ +0.1367, +0.1107, -0.0148, +0.1158, -0.0820, +0.3047]) + +if __name__=="__main__": + demo_run() diff --git a/examples/pybullet/gym/enjoy_TF_PybulletHopper_v0_2017may.py b/examples/pybullet/gym/enjoy_TF_PybulletHopper_v0_2017may.py new file mode 100644 index 000000000..6618b9ad2 --- /dev/null +++ b/examples/pybullet/gym/enjoy_TF_PybulletHopper_v0_2017may.py @@ -0,0 +1,279 @@ +import gym +import numpy as np +import pybullet as p +import pybulletgym.envs +import time + +def relu(x): + return np.maximum(x, 0) + +class SmallReactivePolicy: + "Simple multi-layer perceptron policy, no internal state" + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 128) + assert weights_dense2_w.shape == (128, 64) + assert weights_final_w.shape == (64, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x + +def demo_run(): + env = gym.make("PybulletHopper-v0") + + cid = p.connect(p.SHARED_MEMORY) # only show graphics if the browser is already running.... + if cid < 0: + cid = p.connect(p.DIRECT) + + pi = SmallReactivePolicy(env.observation_space, env.action_space) + + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() + + while 1: + time.sleep(0.01) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + still_open = env.render("human") + if still_open==False: + return + if not done: continue + if restart_delay==0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60*2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay==0: break + +weights_dense1_w = np.array([ +[ +0.6734, +1.1364, +0.6117, +0.5185, 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-0.5620, -0.1466, -0.1626, -0.3288, -0.0070, +0.0508, -0.3687, -0.4245, -0.2751] +]) + +weights_dense2_b = np.array([ +0.0266, +0.3395, +0.0968, +0.0097, +0.0443, -0.1722, -0.2537, +0.1501, +0.0233, -0.0174, +0.1783, -0.0513, -0.1531, -0.2145, +0.0299, -0.1589, +0.1143, +0.1132, -0.2700, -0.0481, +0.2876, +0.1824, +0.2418, +0.1009, -0.1451, +0.1637, +0.1703, +0.0336, -0.1405, -0.0162, -0.1158, +0.1691, -0.0223, -0.1828, +0.2352, +0.0209, -0.1362, -0.2636, +0.1271, -0.0936, -0.1436, +0.0606, +0.1311, -0.0989, -0.2877, +0.2593, +0.0781, -0.1118, -0.1264, +0.2478, +0.2398, -0.1515, +0.0959, -0.0821, -0.1740, +0.1116, -0.0682, +0.1743, +0.1746, -0.1950, -0.0951, +0.2190, -0.0294, +0.1950]) + +weights_final_w = np.array([ +[ -0.6826, +0.0812, +0.5225], +[ -0.2533, +0.7043, +0.6677], +[ +0.0684, +0.6807, +0.4726], +[ +0.4605, +0.3736, -0.3187], +[ +0.4913, +0.0049, +0.4186], +[ +0.3066, +0.3756, -0.3588], +[ -0.3078, +0.3643, -0.0335], +[ -0.5433, +0.5926, -0.1254], +[ -0.2532, +0.7950, 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-0.2911], +[ +0.8250, -0.0788, -0.3731], +[ +0.2661, -0.2775, +0.2854], +[ -0.1036, +0.2179, -0.6408], +[ -0.5371, -0.7837, +0.1141], +[ +0.4754, -0.0923, +0.1714], +[ +0.0448, +0.4901, -0.1578], +[ +0.8797, +0.3316, +0.4349], +[ +0.1514, -0.4129, -0.3333], +[ +0.2847, +0.2285, +0.1408], +[ +0.0045, +0.3333, +0.0694], +[ +0.1553, -0.5147, +0.0549], +[ -1.0765, -0.5130, +0.1472], +[ +0.0808, -0.2612, +0.2591], +[ +0.5104, +0.3062, +0.3979], +[ -0.0758, -0.4512, -0.4422], +[ +0.3236, +0.4479, -0.1759], +[ +0.2181, -0.0028, +0.5615], +[ -0.0254, -0.1924, +0.4608], +[ -0.1566, +0.5634, -0.2498], +[ -0.0042, +0.5403, +0.0272], +[ +0.1206, +0.3941, -1.1469], +[ +0.7642, -1.0114, +0.4905], +[ +0.5071, +0.2270, +0.0046] +]) + +weights_final_b = np.array([ +0.4868, -0.0987, -0.0946]) + +if __name__=="__main__": + demo_run() diff --git a/examples/pybullet/gym/enjoy_TF_PybulletHumanoid_v0_2017may.py b/examples/pybullet/gym/enjoy_TF_PybulletHumanoid_v0_2017may.py new file mode 100644 index 000000000..caf7d8556 --- /dev/null +++ b/examples/pybullet/gym/enjoy_TF_PybulletHumanoid_v0_2017may.py @@ -0,0 +1,501 @@ +import gym +import numpy as np +import pybullet as p +import pybulletgym.envs +import time + +def relu(x): + return np.maximum(x, 0) + +class SmallReactivePolicy: + "Simple multi-layer perceptron policy, no internal state" + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 256) + assert weights_dense2_w.shape == (256, 128) + assert weights_final_w.shape == (128, action_space.shape[0]) + + def act(self, ob): + ob[0] += -1.4 + 0.8 + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x + +def demo_run(): + env = gym.make("PybulletHumanoid-v0") + + cid = p.connect(p.SHARED_MEMORY) # only show graphics if the browser is already running.... + if cid < 0: + cid = p.connect(p.DIRECT) + + pi = SmallReactivePolicy(env.observation_space, env.action_space) + + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() + + while 1: + time.sleep(0.01) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + still_open = env.render("human") + if still_open==False: + return + if not done: continue + if restart_delay==0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60*2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay==0: break + +weights_dense1_w = np.array([ +[ +0.2289, +0.2584, +0.2595, +0.0173, +0.1293, -0.2980, +0.1410, +0.0982, +0.0216, +0.3724, +0.2204, -0.0734, -0.2420, -0.3443, -0.2738, -0.3825, +0.1504, -0.0930, -0.2680, +0.0685, +0.1592, +0.2534, -0.0787, -0.0426, +0.2591, +0.2134, -0.1631, +0.5168, +0.1444, +0.2736, +0.3623, -0.3472, +0.0393, -0.3056, +0.3850, -0.5231, +0.4511, +0.4223, -0.0905, +0.2265, +0.1662, +0.1092, +0.0426, -0.0209, +0.3260, -0.1788, +0.5045, 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+0.0915, -0.0571, -0.1035, -0.0038, -0.1508, -0.2323, -0.1788], +[ +0.1543, -0.0994, +0.0674, +0.1953, +0.1254, -0.1263, +0.0854, -0.1048, +0.0106, -0.0203, +0.1019, -0.0881, +0.2788, -0.2237, -0.0723, +0.0393, -0.0969], +[ +0.0560, +0.2425, +0.1031, +0.1155, -0.1187, -0.1155, -0.0789, +0.2113, +0.0024, +0.2648, -0.1811, -0.0259, -0.1363, -0.2149, -0.2199, -0.1105, -0.1616], +[ -0.0967, +0.0007, -0.2230, +0.1197, +0.1085, +0.0267, -0.0633, +0.1611, -0.0078, +0.0088, -0.0879, -0.1362, +0.1364, -0.1784, +0.1725, -0.0491, -0.0177], +[ +0.0724, -0.0574, +0.0696, -0.0095, -0.1601, +0.1243, +0.2373, -0.1058, +0.1610, -0.1874, -0.0689, +0.1806, -0.2117, -0.0855, -0.0657, +0.0366, -0.0797] +]) + +weights_final_b = np.array([ -0.0356, +0.0776, -0.0344, +0.1375, +0.1048, +0.3648, +0.3240, +0.1319, +0.1161, +0.3373, +0.3193, +0.0120, +0.0253, -0.2434, -0.1291, +0.1042, -0.2448]) + +if __name__=="__main__": + demo_run() diff --git a/examples/pybullet/gym/enjoy_TF_PybulletInvertedDoublePendulum_v0_2017may.py b/examples/pybullet/gym/enjoy_TF_PybulletInvertedDoublePendulum_v0_2017may.py new file mode 100644 index 000000000..a4978ec65 --- /dev/null +++ b/examples/pybullet/gym/enjoy_TF_PybulletInvertedDoublePendulum_v0_2017may.py @@ -0,0 +1,178 @@ +import gym +import numpy as np +import pybullet as p +import pybulletgym.envs +import time + +def relu(x): + return np.maximum(x, 0) + +class SmallReactivePolicy: + "Simple multi-layer perceptron policy, no internal state" + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 64.0) + assert weights_dense2_w.shape == (64.0, 32.0) + assert weights_final_w.shape == (32.0, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x + +def demo_run(): + env = gym.make("PybulletInvertedDoublePendulum-v0") + + cid = p.connect(p.SHARED_MEMORY) # only show graphics if the browser is already running.... + if cid < 0: + cid = p.connect(p.DIRECT) + + pi = SmallReactivePolicy(env.observation_space, env.action_space) + + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() + + while 1: + time.sleep(0.05) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + still_open = env.render("human") + if still_open==False: + return + if not done: continue + if restart_delay==0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60*2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay > 0: continue + break + +weights_dense1_w = np.array([ +[ -0.5857, +0.1810, +0.2839, +0.1278, -0.4302, -0.3152, +0.5916, -0.0635, +0.6259, +0.2873, -0.0572, -0.3538, -0.8121, +0.2707, +0.1656, -0.2103, -0.1614, -0.2789, -0.5856, -0.4733, +0.1838, +0.1063, +0.7629, +0.0873, +0.1480, +0.1768, +0.6522, +0.1158, -0.0816, +0.6542, -0.8870, +0.1775, +0.1532, +0.2268, -0.0313, -0.0470, +0.5328, -0.0570, +0.4820, -0.3772, -0.7581, +0.2835, -0.3566, +0.9371, -0.0441, -0.1797, -0.2859, -0.0238, +0.0261, -0.0296, -0.1406, +0.2869, +0.1279, +0.6653, +0.5643, -0.3136, +0.7751, +0.2341, +0.1903, +0.8283, -0.0697, +0.1276, -0.0250, -0.0053], +[ +0.3741, +0.4844, -0.0638, -0.3205, +0.3137, +0.9636, +0.5329, +0.6882, +0.2983, -0.6675, -0.6372, +0.2065, -0.2645, -0.4789, +0.2326, -0.0691, -0.5905, -0.3354, +0.3428, +0.4253, +0.9111, -0.4751, -0.2124, +0.3920, +0.2897, -1.1101, +0.1894, -0.4025, -0.1125, -0.0627, +0.2347, -0.8787, +0.1019, +0.9128, +0.2544, -0.3933, +0.6485, -0.1936, -0.2402, +0.5012, -0.0918, +0.3160, -0.7860, +0.3439, -0.4268, -0.1788, -0.3930, +0.5128, +0.2338, +0.2571, +0.1343, +0.9850, -0.7074, +0.3532, +0.3048, -0.4542, +0.5539, -0.4409, -0.2003, -0.4837, -0.3554, -0.4447, -0.0817, -0.8497], +[ +0.0825, +0.5847, +0.4837, +0.5144, +0.4770, +0.0199, +0.4275, -0.4530, +0.8499, -0.2840, +0.3817, -0.5098, -0.2155, -0.1475, +0.1145, -0.1871, -0.0526, +0.3583, -0.3537, -0.7111, -0.6116, +0.3406, -0.6360, +0.7786, +0.6628, -0.0493, +0.3505, -0.0376, -0.6556, +1.0748, -0.5329, +0.6477, -0.7117, +0.3723, +0.6006, +0.0171, +0.0012, +0.4910, -0.5651, -0.6868, +0.2403, +0.0254, -0.4416, +0.7534, -0.0138, -1.1298, +0.5447, +0.0974, +0.1988, -0.2161, -0.3126, -0.5731, -0.1278, +0.2995, -0.1200, -0.7917, +0.5326, +0.4562, -0.0144, +0.5717, -0.4065, +0.1494, +0.7100, +0.2461], +[ -0.2861, +0.4314, -0.2982, -0.1401, -0.1033, +0.5287, -0.6620, -0.3975, +0.0038, +0.1991, -0.7079, -0.9000, +0.1659, +0.3623, -0.0752, -0.1907, -0.2335, -0.5143, +0.2324, -0.0487, +0.1583, -0.5989, +0.5957, +0.2150, -0.0335, +0.2154, +0.3279, -0.7976, +0.5320, -0.4438, +0.2170, -0.3841, -0.0039, -0.0847, -0.0028, -0.4278, -0.2393, -0.9239, +0.2880, -0.1437, -0.0941, -0.0796, -0.3906, -0.3224, +0.1038, -0.1929, -0.2713, -0.4157, -0.2178, +0.5729, -0.2065, +0.0059, +0.3879, +0.0590, +0.1759, +0.0677, -0.0170, -0.2252, +0.3301, -0.0599, +0.3791, -0.1455, +0.2200, -0.0499], +[ -0.4403, +0.7182, +0.7941, +0.1469, +1.5777, +0.3426, +0.0923, +0.2160, +1.1492, -0.5206, -0.2659, -0.1504, +0.2739, -1.3939, +0.8992, -1.1433, -0.3828, -0.2497, -0.2172, +0.2318, -0.3605, +0.6413, -1.9095, +1.4785, -0.1274, -0.7208, -0.0802, -0.8779, -1.6260, +0.9151, +0.8289, -0.0902, -0.3551, +0.6198, +1.7488, +0.0739, -1.2022, -0.3536, -1.5187, +0.1839, +1.4258, +0.4217, +0.1503, -0.0460, +0.2327, -0.4139, -0.3668, +0.2997, +0.6856, +0.6917, -0.3856, -0.3620, +0.1578, -0.8349, -1.0796, -0.0319, -1.1966, -0.8122, +0.5053, -0.5033, -0.9207, -0.1193, -0.7625, +0.1379], +[ -0.0321, -0.3206, -0.4516, +0.3420, +1.0964, +0.0311, +0.4654, -0.2367, +0.3347, -0.2798, -0.8169, -0.1555, +0.9397, -0.5597, +0.7113, -0.3642, -0.2840, -0.1323, -0.1000, +0.2283, +0.3612, -0.4784, +0.0504, +0.5310, -0.0887, +0.2926, +0.5069, -0.5645, -0.0976, -0.2594, +0.4425, +0.9223, -0.5637, -0.2336, -0.1316, 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-0.5510], +[ -1.0587], +[ +0.6996], +[ -0.5009], +[ -0.4000], +[ -0.6958], +[ +0.7716], +[ -0.5342], +[ -0.5095], +[ +0.3040], +[ -1.1986], +[ -0.4900], +[ +0.7726], +[ +0.5871], +[ -0.2533], +[ +0.2633], +[ -0.0004] +]) + +weights_final_b = np.array([ +0.0190]) + +if __name__=="__main__": + demo_run() diff --git a/examples/pybullet/gym/enjoy_TF_PybulletInvertedPendulumSwingup_v0_2017may.py b/examples/pybullet/gym/enjoy_TF_PybulletInvertedPendulumSwingup_v0_2017may.py new file mode 100644 index 000000000..4b64cc4fa --- /dev/null +++ b/examples/pybullet/gym/enjoy_TF_PybulletInvertedPendulumSwingup_v0_2017may.py @@ -0,0 +1,174 @@ +import gym, roboschool +import numpy as np +import pybullet as p +import pybulletgym.envs +import time + +def relu(x): + return np.maximum(x, 0) + +class SmallReactivePolicy: + "Simple multi-layer perceptron policy, no internal state" + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 64.0) + assert weights_dense2_w.shape == (64.0, 32.0) + assert weights_final_w.shape == (32.0, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x + +def demo_run(): + env = gym.make("PybulletInvertedPendulumSwingup-v0") + + cid = p.connect(p.SHARED_MEMORY) # only show graphics if the browser is already running.... + if cid < 0: + cid = p.connect(p.DIRECT) + + pi = SmallReactivePolicy(env.observation_space, env.action_space) + + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() + + while 1: + time.sleep(0.05) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + still_open = env.render("human") + if still_open==False: + return + if not done: continue + if restart_delay==0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60*2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay > 0: continue + break + +weights_dense1_w = np.array([ +[ +0.5877, -0.5825, -0.5542, -0.2557, -0.4485, +1.4126, +0.2701, -0.6204, -0.2580, +0.2106, -0.2296, +0.7949, +0.6224, -0.0186, +0.4216, +1.0924, -0.1538, -0.2818, +0.4855, -0.2496, +0.7461, -0.6156, +0.0801, +0.7871, -0.4312, -0.9585, +0.1566, -0.2218, -1.0393, +0.6104, -0.5339, +0.8258, +0.4064, +0.0503, +0.4753, -0.8161, +0.0812, +0.2311, -0.9492, -1.1791, +1.2375, +0.2916, +1.2290, +0.2796, -0.8864, -1.1424, -0.5714, +0.1413, +0.7340, -0.4152, +0.2832, -0.3886, +0.4810, -0.7092, -0.5966, +0.1089, +0.1007, +0.5226, -0.3343, +0.1760, +0.4099, -0.9913, -1.1694, -1.0018], +[ +0.4054, +0.2495, +0.5483, +0.7193, -0.1833, -0.2237, -0.4353, -0.1005, +0.2848, +0.3193, +0.2551, -0.1267, -0.7200, +0.3952, +0.3390, +0.2123, +0.1388, +0.8869, -0.1095, -0.1718, -0.4128, -0.7047, -1.1383, +0.6552, -0.0037, -0.4306, +0.2749, -0.9121, +0.4406, -0.0163, +0.4852, +0.6150, +0.1354, -0.7839, +0.2261, +0.3988, -0.2867, -0.5369, -0.0788, +0.0125, +0.2645, +0.1614, +0.7531, +0.5786, +0.6903, -0.7974, -0.2934, -0.3407, -0.7366, -0.1585, +1.0333, -0.0183, +0.2690, -0.5674, -0.0266, +0.0898, -0.1441, -0.0988, +0.7260, +0.7994, +0.1521, -0.3210, -0.1403, -0.2685], +[ -0.1050, -0.1826, +0.4717, -0.3515, +0.9648, -0.6372, -0.4686, +0.6959, +0.3540, +0.3515, +0.3239, -1.6177, -0.0651, +0.4653, +0.5058, +0.3465, -0.6693, -0.1118, -0.9582, -1.5053, -0.2256, -0.1989, -0.1901, -0.4282, -1.3479, -0.5629, +0.6828, -0.3515, -0.4724, +0.4618, +0.3008, +0.1280, +0.3720, -0.0545, +0.3104, -0.2527, +0.4614, +0.4994, -0.0099, +0.4597, -0.2667, -0.0374, -0.3393, +0.2675, -0.2635, -0.6062, +0.6404, +0.4500, -0.5105, -1.5838, -0.1396, +0.8804, +0.5794, -0.6823, -0.2125, +0.4510, +0.2424, +0.3407, -0.3354, +0.1306, -1.0006, +0.2358, +0.6479, +0.2027], +[ +0.7453, +0.8937, -0.9068, +0.2950, +0.4412, -0.6005, -1.3008, -0.0299, -0.6434, +1.4992, +0.7437, +0.4271, -0.0549, +1.2337, +1.6758, -0.7335, +0.2251, 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+0.2080] +]) + +weights_dense1_b = np.array([ -0.4441, -0.2462, -0.2997, +0.3283, -0.2751, +0.0474, +0.0720, -0.2133, -0.0770, -0.0053, +0.0138, -0.3554, -0.2999, -0.2340, +0.0054, +0.4380, -0.1461, -0.2035, -0.8094, +0.0909, -0.1714, +0.2412, -0.1519, +0.0391, +0.1525, +0.1798, +0.1041, +0.4503, -0.0088, +0.0323, -0.0414, +0.4621, +0.1720, -0.1793, +0.1734, +0.1588, +0.2802, +0.1220, +0.1011, -0.1334, -0.0663, +0.4778, +0.1110, -0.1536, +0.1873, -0.0090, -0.5979, +0.3604, -0.2515, +0.4471, +0.2444, -0.2565, -0.1102, +0.0982, -0.0625, +0.3902, -0.0248, -0.2240, +0.0894, -0.0671, -0.3344, -0.0089, -0.0793, +0.2673]) + +weights_dense2_w = np.array([ +[ +0.1063, +0.2017, +0.0029, -0.2442, -0.1362, +0.2871, +0.2270, -0.1260, +0.5271, -0.1744, -0.4323, +0.3637, -0.0083, -0.0547, +0.4549, -0.0164, +0.0913, -0.1635, +0.3583, +0.3020, +0.2240, -0.3561, +0.0689, +0.0126, +0.0508, -1.2876, -0.0003, -0.0464, -0.2184, -0.2538, -0.5314, +0.5790], +[ -0.2180, +0.9455, +0.1446, -0.0724, +0.3771, 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-0.5576, +0.1040, -0.1875, +0.1399, -0.1539, -1.3570, -0.1105, +0.0370, -0.4067, +0.2991, +0.2811, +0.1082, -0.0573, +0.2104, -0.1550, +0.3365, +0.5019, +0.4842, +0.4671, +0.2578, -0.0029, +0.0016, -0.1533, -0.2459, +0.1866, +0.0699, -0.1873, -1.1082, -0.9151, -0.1758], +[ +0.2397, +0.2045, -0.2370, -0.3293, -0.3153, -0.2131, +0.1407, -0.0721, +0.0723, -0.0019, -0.3940, +0.1340, +0.3550, +0.1190, -0.6068, -0.0747, -0.7712, +0.1922, +0.6519, -0.0651, -0.4332, +0.0494, -0.7192, +0.4279, -0.1762, -0.5548, +0.1749, -0.2149, -0.6916, -0.0448, -0.1025, +0.0212], +[ -0.1101, -0.2853, -0.3405, +0.3059, -0.5009, +0.1139, +0.0602, -0.5256, -0.9340, +0.1189, -0.9900, -0.5092, -1.9114, +0.1249, -0.7890, -1.1437, -0.4686, +0.3687, -0.2993, -0.1058, +0.0966, -0.0284, -0.4845, -0.1683, +0.3489, -0.0173, -0.0521, -0.1265, -0.0182, -0.2870, +0.0246, -1.0009], +[ -0.1411, +0.1840, -0.3968, +0.2893, -0.9532, -0.2235, -0.1156, -0.7018, -0.2859, -0.1742, -0.6094, -0.0247, -1.0472, +0.1916, -0.4825, -0.4209, +0.2371, +0.0900, +0.0646, -0.1665, +0.5168, +0.0670, -0.1779, +0.3494, +0.3035, +0.0548, +0.2939, -0.3871, -0.0828, -0.5370, +0.0804, -0.2175], +[ +0.4992, +0.1187, -0.0464, +0.7284, +0.1106, -0.0542, -0.3548, +0.3451, +0.0281, -0.4796, -0.2282, -0.3789, -0.1253, -0.0824, -0.3919, +0.1890, -0.1683, -2.1362, -0.9594, -0.6882, +0.6158, -0.2412, +0.2336, -0.0142, -0.9257, +0.3819, -0.1836, -0.7676, +0.3713, -0.1364, +0.3317, +0.3696] +]) + +weights_dense2_b = np.array([ -0.0528, +0.0930, -0.3614, +0.2145, -0.3644, -0.0033, -0.0702, -0.0928, -0.1018, +0.0424, +0.0130, +0.2634, -0.1167, +0.2412, +0.0852, +0.0047, +0.1958, -0.1322, +0.0218, +0.2207, +0.1946, +0.0936, +0.2900, +0.2404, -0.1711, +0.1214, +0.2968, -0.2935, -0.0390, +0.1330, +0.0325, +0.2185]) + +weights_final_w = np.array([ +[ -0.2378], +[ +0.1955], +[ -0.2006], +[ -0.5372], +[ -0.3298], +[ +0.0891], +[ -0.3930], +[ +0.8978], +[ +0.3177], +[ +0.5357], +[ +0.2878], +[ +0.4998], +[ +0.2550], +[ -0.2619], +[ +1.1990], +[ +0.3115], +[ +0.3655], +[ +0.5774], +[ -0.4641], +[ +0.2613], +[ +0.1928], +[ +0.1458], +[ +0.4138], +[ -0.4969], +[ +0.4147], +[ +1.0689], +[ -0.1562], +[ -0.3669], +[ -0.3073], +[ +0.3354], +[ +0.9354], +[ +0.8831] +]) + +weights_final_b = np.array([ +0.2753]) + +if __name__=="__main__": + demo_run() diff --git a/examples/pybullet/gym/enjoy_TF_PybulletInvertedPendulum_v0_2017may.py b/examples/pybullet/gym/enjoy_TF_PybulletInvertedPendulum_v0_2017may.py new file mode 100644 index 000000000..424fe85bb --- /dev/null +++ b/examples/pybullet/gym/enjoy_TF_PybulletInvertedPendulum_v0_2017may.py @@ -0,0 +1,173 @@ +import gym +import numpy as np +import pybullet as p +import pybulletgym.envs +import time + +def relu(x): + return np.maximum(x, 0) + +class SmallReactivePolicy: + "Simple multi-layer perceptron policy, no internal state" + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 64) + assert weights_dense2_w.shape == (64, 32) + assert weights_final_w.shape == (32, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x + +def demo_run(): + env = gym.make("PybulletInvertedPendulum-v0") + + cid = p.connect(p.SHARED_MEMORY) # only show graphics if the browser is already running.... + if cid < 0: + cid = p.connect(p.DIRECT) + + pi = SmallReactivePolicy(env.observation_space, env.action_space) + + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() + + while 1: + time.sleep(0.05) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + still_open = env.render("human") + if still_open==False: + return + if not done: continue + if restart_delay==0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60*2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay==0: break + +weights_dense1_w = np.array([ +[ +0.8621, +0.3260, +0.0986, -0.1225, +0.2038, -0.8051, -0.7498, +0.1905, -0.3418, +0.5002, -0.1093, +0.0285, +0.3480, -0.1596, -0.1781, +0.3643, -0.4283, -0.3715, -0.1571, +0.3531, +0.0934, -0.2215, -0.3085, +0.9581, +0.2485, -0.6232, -0.3175, +0.9771, +0.3651, -0.8850, -0.4212, -0.0301, +0.0432, +0.3390, +0.7537, +0.1649, -0.0128, -0.1374, +0.3793, +1.0430, +0.8043, -0.9001, +0.4334, -0.1243, +1.2373, +0.1890, +0.3333, -0.0520, +0.1654, +0.2521, -0.0168, +0.8439, -0.6960, +0.1884, +0.0991, +0.5242, -0.6837, +0.6844, -0.2593, -0.3298, +0.2212, +0.0281, +0.2608, +0.6527], +[ -0.9350, -0.2122, +0.0162, +0.5306, -0.2914, -0.8573, +0.2552, +0.7069, +0.7862, -0.0315, -1.0844, +0.2707, +0.5102, -1.1359, +0.3066, +0.0357, +0.1833, -0.1946, +0.0948, +0.6685, -0.6101, +0.4774, -0.3017, +0.3823, -0.2835, -0.6760, +1.2963, +0.4466, -0.7132, -0.9109, -0.0589, -0.8726, +0.6972, -0.2256, -0.0286, +0.4646, -0.5113, -0.1692, +0.7638, +0.2274, -0.5734, +0.7430, +0.9680, +0.7809, -0.2457, -0.4952, +0.0197, -0.6428, +0.2367, -0.5887, -0.5167, +0.2299, -0.5853, -0.4101, +0.9042, +0.0913, +0.5774, +0.2756, +0.2436, -0.6068, -0.2232, -0.1415, -0.5094, -0.1012], +[ +0.0983, -0.3266, +0.2611, +0.0664, +0.6222, +0.0773, -0.2516, -0.4416, -0.3770, +0.0535, +0.3391, -0.7475, +0.5874, -0.0405, -0.2058, -0.5957, +0.2659, -0.8477, -0.5814, -0.0494, -0.1678, +0.2650, -0.4039, +0.1414, -0.6635, +0.0447, +0.2932, +0.1167, +0.1195, +0.0669, -0.4223, +0.1196, +0.0553, -0.7123, -0.4011, +0.3557, -0.4503, +0.7047, -0.4471, +0.0807, +0.3926, -0.1427, +0.4355, -0.3678, +0.3453, +0.1597, -0.3076, +0.4689, +0.3128, -0.7050, +0.6505, +0.3427, +0.1981, +0.1190, +0.2554, +0.8283, +0.1647, -0.4257, +0.1481, +0.4361, -0.5497, -0.6114, -0.0138, +0.0932], +[ +0.1866, +0.6408, -1.8201, +1.0946, +0.7742, -0.7651, +0.1082, +0.6842, +0.3794, +0.3547, -0.8172, -0.0921, -0.6736, +1.0251, -0.9618, -0.6869, +1.8465, +0.2425, +0.7910, +1.0009, -0.8031, +1.6697, -0.8962, +0.1873, +0.4960, -0.6812, +0.6860, +1.1932, -0.7019, +0.4028, +0.4841, +0.6497, +1.6490, -0.5464, +0.7060, +1.8087, -0.6118, -0.7955, -0.3797, -1.2048, +1.2356, -0.6141, +1.2502, +0.5641, -0.1019, -1.7516, -0.1134, -0.6719, +1.5014, -0.2718, -0.5933, +0.1714, -1.3590, -0.3656, +1.0083, -0.8511, -0.5597, -0.4446, -1.7158, -0.0851, +0.3089, +0.0967, -1.0121, +0.3048], +[ +0.3329, +0.5382, +0.1585, +0.8205, +0.5510, +0.2796, -0.7120, +0.3434, +0.2931, +1.2275, +0.4191, -0.6828, -0.5091, +0.8408, -0.3101, -0.5183, +0.2651, +0.2073, -0.1383, +0.6539, -0.2167, +0.7798, -0.5690, +0.3750, +0.4358, +0.6537, -0.2202, -0.0563, +0.6605, +0.4599, -0.5327, +0.6610, +0.8387, +0.1887, -0.2593, +0.7904, -0.3567, +0.4121, +0.4378, -0.2935, +0.1291, +0.0021, -0.3416, -0.5920, -0.2895, -0.4610, +0.7380, -0.6322, +0.6738, -0.1378, -0.3304, -0.2894, -0.3582, -0.8311, +0.2660, -0.2079, -0.1765, -0.6825, -0.1754, -0.4455, +0.7202, -0.8177, -0.9900, -0.7425] +]) + +weights_dense1_b = np.array([ +0.0009, -0.2179, -0.0299, +0.2069, -0.0099, +0.0907, +0.0271, +0.1957, +0.0185, +0.1671, -0.0699, -0.0332, -0.0244, +0.0022, +0.1877, -0.0801, +0.0235, -0.0097, -0.0088, +0.1961, -0.1055, +0.0605, -0.0913, +0.0884, -0.0638, +0.0229, -0.1101, +0.1966, -0.0042, +0.0221, -0.0966, +0.1554, +0.1623, -0.0454, +0.1068, +0.0114, +0.0544, +0.0201, +0.0257, +0.0637, +0.0761, +0.2120, +0.0225, +0.2023, -0.0931, +0.0585, -0.2253, -0.0302, +0.0682, +0.0000, -0.1215, -0.1105, +0.1376, +0.0583, -0.1706, -0.0262, -0.0897, +0.0728, +0.0787, +0.0912, -0.0964, -0.0959, -0.0195, +0.0232]) + +weights_dense2_w = np.array([ +[ +0.0089, +0.2241, -0.0391, +0.1459, -0.0854, -0.0878, +0.2829, -0.1620, -0.1694, -0.5211, +0.0155, -0.1298, -0.0629, +0.1074, +0.0150, -0.3583, +0.0427, +0.1813, +0.2140, -0.4230, +0.1577, +0.1223, -0.0096, +0.0183, -0.1038, -0.5612, -0.0614, -0.0820, -0.0057, -0.2471, +0.0355, -0.1525], +[ +0.1555, -0.2934, +0.2690, -0.3218, +0.0101, -0.1188, -0.1798, -0.1405, +0.2701, -0.0972, +0.2338, -0.0122, -0.2254, -0.3225, -0.0268, -0.0829, +0.4085, +0.0691, -0.1448, -0.0429, -0.2750, -0.2479, +0.0396, +0.0427, +0.0205, -0.1462, -0.1481, +0.1365, -0.0903, +0.0094, +0.3665, +0.1163], +[ +0.0119, -0.3100, +0.1201, -0.2257, +0.1246, -0.1335, -0.3369, -0.0408, +0.3145, +0.2030, +0.1506, +0.0899, -0.1192, -0.2429, +0.0356, +0.0634, -0.0706, +0.1119, -0.0402, +0.1011, +0.1281, +0.4318, -0.4644, +0.0039, +0.0932, -0.0521, -0.1528, +0.1946, -0.0921, -0.0646, -0.0241, +0.1598], +[ -0.1007, +0.3939, -0.2066, +0.0752, -0.1709, -0.0286, +0.0196, +0.1853, -0.3619, -0.0449, +0.0334, -0.2673, +0.0640, +0.3055, -0.1184, -0.4550, +0.0951, -0.2168, +0.1502, -0.4816, +0.1392, -0.3708, -0.0849, -0.4331, -0.0800, -0.0967, +0.1334, -0.3169, -0.0004, -0.3002, -0.0841, -0.1763], +[ -0.0492, +0.0308, +0.0824, +0.0568, -0.0038, +0.3196, +0.5089, +0.0391, -0.1373, -0.1579, +0.0219, -0.2990, -0.0113, -0.2136, -0.0240, +0.1241, -0.1723, -0.0064, -0.0213, -0.2213, -0.0996, -0.0333, -0.4110, 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+0.0285], +[ -0.1548], +[ +0.2539], +[ -0.2544], +[ +0.2006], +[ -0.4121], +[ -0.2084], +[ -0.0381], +[ +0.2733], +[ -0.3076], +[ +0.0013], +[ +0.0957], +[ -0.1298], +[ -0.1112] +]) + +weights_final_b = np.array([ +0.0352]) + +if __name__=="__main__": + demo_run() diff --git a/examples/pybullet/gym/enjoy_TF_PybulletWalker2d_v0_2017may.py b/examples/pybullet/gym/enjoy_TF_PybulletWalker2d_v0_2017may.py new file mode 100644 index 000000000..f382e9114 --- /dev/null +++ b/examples/pybullet/gym/enjoy_TF_PybulletWalker2d_v0_2017may.py @@ -0,0 +1,286 @@ +import gym +import numpy as np +import pybullet as p +import pybulletgym.envs +import time + +def relu(x): + return np.maximum(x, 0) + +class SmallReactivePolicy: + "Simple multi-layer perceptron policy, no internal state" + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 128) + assert weights_dense2_w.shape == (128, 64) + assert weights_final_w.shape == (64, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x + +def demo_run(): + env = gym.make("PybulletWalker2d-v0") + + cid = p.connect(p.SHARED_MEMORY) # only show graphics if the browser is already running.... + if cid < 0: + cid = p.connect(p.DIRECT) + + pi = SmallReactivePolicy(env.observation_space, env.action_space) + + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() + + while 1: + time.sleep(0.01) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + still_open = env.render("human") + if still_open==False: + return + if not done: continue + if restart_delay==0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60*2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay==0: break + +weights_dense1_w = np.array([ +[ -0.5923, 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+0.3863, +0.0820, +0.1836, -0.1025, -0.0837, +0.3728], +[ +0.1172, +0.2514, -0.2447, -0.0147, +0.3737, -0.1232], +[ +0.4822, -0.1587, +0.0189, +0.1085, -0.2107, +0.1819], +[ -0.3112, +0.2384, -0.0983, +0.3085, +0.3343, -0.1824], +[ +0.1329, -0.7842, +0.5067, -0.1467, +0.6028, -0.1696], +[ +0.0913, -0.2866, +0.1866, +0.0102, -0.2893, +0.1818], +[ -0.0842, +0.0903, +0.0537, +0.2681, +0.6005, -0.3763], +[ +0.4081, -0.5070, -0.2256, +0.4282, -0.2310, +0.0178], +[ +0.1979, +0.1552, +0.7038, -0.0276, +0.1008, +0.4311], +[ +0.0105, +0.0500, -0.5326, -0.1960, -0.0029, +0.7599], +[ +0.0132, +0.1029, +0.0586, +0.2432, -0.0545, -0.3576], +[ -0.1861, +0.2730, -0.1026, +0.2567, +0.1899, +0.0994], +[ +0.2804, +0.4976, +0.0234, -0.0693, +0.1044, -0.1782], +[ +0.1651, -0.0445, -0.1732, -0.1439, +0.1910, -0.2900], +[ +0.0575, +0.0506, -0.5541, -0.0837, +0.1331, +0.0596], +[ -0.5366, -0.2095, +0.2158, +0.1478, +0.1552, -0.2847], +[ -0.2239, -0.5064, +0.2553, +0.6997, -0.2814, -0.5053], +[ -0.5875, -0.2929, -0.3054, +0.4141, +0.0547, +0.2617], +[ +0.4712, +0.1022, +0.2665, +0.1481, +0.2837, +0.3440], +[ +0.3937, +0.3063, +0.4430, -0.5654, +0.0932, -0.1030], +[ +0.0561, +0.7068, +0.1140, -0.0612, +0.1575, -0.4454], +[ -0.4082, -0.3756, -0.0447, +0.5955, +0.2170, +0.2459], +[ +0.3779, +0.1477, -0.2120, -0.3360, -0.3584, +0.1333], +[ -0.1877, +0.1041, -0.2141, -0.0261, +0.0121, +0.5809], +[ +0.5229, +0.3220, +0.4367, -0.1934, +0.0125, +0.3250], +[ -0.0197, -0.0964, +0.6821, +0.0876, +0.1429, -0.2392], +[ -0.3900, +0.3254, -0.1975, +0.2021, -0.4072, +0.2072], +[ +0.2660, -0.2046, +0.2172, +0.2520, +0.1637, -0.0179], +[ -0.0649, -0.0451, +0.0873, +0.0077, -0.3684, -0.0634], +[ +0.1304, +0.2106, +0.0804, +0.0554, -0.2718, -0.4081], +[ +0.3119, +0.1979, -0.2498, +0.0328, +0.2566, +0.2054], +[ +0.0622, +0.2239, -0.7600, -0.2464, -0.3213, -0.4771], +[ -0.3352, +0.4833, +0.0830, +0.3854, -0.0733, -0.0983], +[ +0.2465, +0.0722, -0.0776, +0.2516, +0.5810, +0.5918], +[ -0.2167, -0.2138, +0.2410, -0.3535, -0.4878, -0.4168], +[ -0.0991, +0.4950, +0.0633, +0.2891, -0.5150, +0.1257], +[ -0.0267, -0.5339, -0.2769, -0.0796, +0.0055, -0.2304], +[ +0.2994, +0.3536, -0.3556, +0.3602, -0.1715, -0.3168], +[ +0.1544, -0.5746, -0.1126, -0.4654, +0.1188, -0.0478], +[ +0.4130, -0.7358, +0.0469, +0.0414, -0.0352, -0.5242], +[ -0.3941, +0.6807, +0.0670, +0.6347, +0.0394, +0.2293], +[ -0.0101, +0.1363, +0.6806, -0.1179, +0.1445, -0.0643], +[ -0.5794, +0.1603, -0.3438, -0.5360, -0.0372, +0.1549], +[ -0.0897, +1.1163, +0.4080, +0.1319, +0.1437, +0.0088], +[ +0.2344, +0.4274, -0.1515, +0.0379, -0.2166, -0.0852] +]) + +weights_final_b = np.array([ +0.0090, +0.2404, +0.1022, +0.1035, +0.0621, -0.0160]) + +if __name__=="__main__": + demo_run() diff --git a/examples/pybullet/gym/envs/gym_forward_walkers.py b/examples/pybullet/gym/envs/gym_forward_walkers.py new file mode 100644 index 000000000..ca24c91e1 --- /dev/null +++ b/examples/pybullet/gym/envs/gym_forward_walkers.py @@ -0,0 +1,233 @@ +from distutils.command.config import config + +from pybulletgym.envs.scene_abstract import SingleRobotEmptyScene +from pybulletgym.envs.scene_stadium import SinglePlayerStadiumScene +from gym_mujoco_xml_env import PybulletMujocoXmlEnv +import gym, gym.spaces, gym.utils, gym.utils.seeding +import numpy as np +import os, sys + +class PybulletForwardWalkersBase(PybulletMujocoXmlEnv): + def __init__(self, fn, robot_name, action_dim, obs_dim, power): + PybulletMujocoXmlEnv.__init__(self, fn, robot_name, action_dim, obs_dim) + self.power = power + self.camera_x = 0 + self.walk_target_x = 1e3 # kilometer away + self.walk_target_y = 0 + + def create_single_player_scene(self): + self.stadium_scene = SinglePlayerStadiumScene(gravity=9.8, timestep=0.0165/4, frame_skip=4) + return self.stadium_scene + + def robot_specific_reset(self): + for j in self.ordered_joints: + j.reset_current_position(self.np_random.uniform( low=-0.1, high=0.1 ), 0) + + self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(self.stadium_scene.ground_plane_mjcf) + self.feet = [self.parts[f] for f in self.foot_list] + self.feet_contact = np.array([0.0 for f in self.foot_list], dtype=np.float32) + self.ground_ids = set([(self.parts[f].bodies[self.parts[f].bodyIndex], self.parts[f].bodyPartIndex) for f in self.foot_ground_object_names]) + self.scene.actor_introduce(self) + self.initial_z = None + + def move_robot(self, init_x, init_y, init_z): + "Used by multiplayer stadium to move sideways, to another running lane." + self.cpp_robot.query_position() + pose = self.cpp_robot.root_part.pose() + pose.move_xyz(init_x, init_y, init_z) # Works because robot loads around (0,0,0), and some robots have z != 0 that is left intact + self.cpp_robot.set_pose(pose) + + def apply_action(self, a): + assert( np.isfinite(a).all() ) + for n,j in enumerate(self.ordered_joints): + j.set_motor_torque( self.power*j.power_coef*float(np.clip(a[n], -1, +1)) ) + + def calc_state(self): + j = np.array([j.current_relative_position() for j in self.ordered_joints], dtype=np.float32).flatten() + # even elements [0::2] position, scaled to -1..+1 between limits + # odd elements [1::2] angular speed, scaled to show -1..+1 + self.joint_speeds = j[1::2] + self.joints_at_limit = np.count_nonzero(np.abs(j[0::2]) > 0.99) + + body_pose = self.robot_body.pose() + parts_xyz = np.array( [p.pose().xyz() for p in self.parts.values()] ).flatten() + self.body_xyz = (parts_xyz[0::3].mean(), parts_xyz[1::3].mean(), body_pose.xyz()[2]) # torso z is more informative than mean z + self.body_rpy = body_pose.rpy() + z = self.body_xyz[2] + r, p, yaw = self.body_rpy + if self.initial_z==None: + self.initial_z = z + self.walk_target_theta = np.arctan2( self.walk_target_y - self.body_xyz[1], self.walk_target_x - self.body_xyz[0] ) + self.walk_target_dist = np.linalg.norm( [self.walk_target_y - self.body_xyz[1], self.walk_target_x - self.body_xyz[0]] ) + angle_to_target = self.walk_target_theta - yaw + + rot_speed = np.array( + [[np.cos(-yaw), -np.sin(-yaw), 0], + [np.sin(-yaw), np.cos(-yaw), 0], + [ 0, 0, 1]] + ) + vx, vy, vz = np.dot(rot_speed, self.robot_body.speed()) # rotate speed back to body point of view + + more = np.array([ + z-self.initial_z, + np.sin(angle_to_target), np.cos(angle_to_target), + 0.3*vx, 0.3*vy, 0.3*vz, # 0.3 is just scaling typical speed into -1..+1, no physical sense here + r, p], dtype=np.float32) + return np.clip( np.concatenate([more] + [j] + [self.feet_contact]), -5, +5) + + def calc_potential(self): + # progress in potential field is speed*dt, typical speed is about 2-3 meter per second, this potential will change 2-3 per frame (not per second), + # all rewards have rew/frame units and close to 1.0 + return - self.walk_target_dist / self.scene.dt + + electricity_cost = -2.0 # cost for using motors -- this parameter should be carefully tuned against reward for making progress, other values less improtant + stall_torque_cost = -0.1 # cost for running electric current through a motor even at zero rotational speed, small + foot_collision_cost = -1.0 # touches another leg, or other objects, that cost makes robot avoid smashing feet into itself + foot_ground_object_names = set(["floor"]) # to distinguish ground and other objects + joints_at_limit_cost = -0.1 # discourage stuck joints + + def _step(self, a): + if not self.scene.multiplayer: # if multiplayer, action first applied to all robots, then global step() called, then _step() for all robots with the same actions + self.apply_action(a) + self.scene.global_step() + + state = self.calc_state() # also calculates self.joints_at_limit + + alive = float(self.alive_bonus(state[0]+self.initial_z, self.body_rpy[1])) # state[0] is body height above ground, body_rpy[1] is pitch + done = alive < 0 + if not np.isfinite(state).all(): + print("~INF~", state) + done = True + + potential_old = self.potential + self.potential = self.calc_potential() + progress = float(self.potential - potential_old) + + feet_collision_cost = 0.0 + for i,f in enumerate(self.feet): + contact_ids = set((x[2], x[4]) for x in f.contact_list()) + #print("CONTACT OF '%s' WITH %s" % (f.name, ",".join(contact_names)) ) + self.feet_contact[i] = 1.0 if (self.ground_ids & contact_ids) else 0.0 + if contact_ids - self.ground_ids: + feet_collision_cost += self.foot_collision_cost + + electricity_cost = self.electricity_cost * float(np.abs(a*self.joint_speeds).mean()) # let's assume we have DC motor with controller, and reverse current braking + electricity_cost += self.stall_torque_cost * float(np.square(a).mean()) + + joints_at_limit_cost = float(self.joints_at_limit_cost * self.joints_at_limit) + + self.rewards = [ + alive, + progress, + electricity_cost, + joints_at_limit_cost, + feet_collision_cost + ] + + self.HUD(state, a, done) + return state, sum(self.rewards), bool(done), {} + + def camera_adjust(self): + x, y, z = self.body_xyz + self.camera_x = 0.98*self.camera_x + (1-0.98)*x + self.camera.move_and_look_at(self.camera_x, y-2.0, 1.4, x, y, 1.0) + +class PybulletHopper(PybulletForwardWalkersBase): + foot_list = ["foot"] + def __init__(self): + PybulletForwardWalkersBase.__init__(self, "hopper.xml", "torso", action_dim=3, obs_dim=15, power=0.75) + def alive_bonus(self, z, pitch): + return +1 if z > 0.8 and abs(pitch) < 1.0 else -1 + +class PybulletWalker2d(PybulletForwardWalkersBase): + foot_list = ["foot", "foot_left"] + def __init__(self): + PybulletForwardWalkersBase.__init__(self, "walker2d.xml", "torso", action_dim=6, obs_dim=22, power=0.40) + def alive_bonus(self, z, pitch): + return +1 if z > 0.8 and abs(pitch) < 1.0 else -1 + def robot_specific_reset(self): + PybulletForwardWalkersBase.robot_specific_reset(self) + for n in ["foot_joint", "foot_left_joint"]: + self.jdict[n].power_coef = 30.0 + +class PybulletHalfCheetah(PybulletForwardWalkersBase): + foot_list = ["ffoot", "fshin", "fthigh", "bfoot", "bshin", "bthigh"] # track these contacts with ground + def __init__(self): + PybulletForwardWalkersBase.__init__(self, "half_cheetah.xml", "torso", action_dim=6, obs_dim=26, power=0.90) + def alive_bonus(self, z, pitch): + # Use contact other than feet to terminate episode: due to a lot of strange walks using knees + return +1 if np.abs(pitch) < 1.0 and not self.feet_contact[1] and not self.feet_contact[2] and not self.feet_contact[4] and not self.feet_contact[5] else -1 + def robot_specific_reset(self): + PybulletForwardWalkersBase.robot_specific_reset(self) + self.jdict["bthigh"].power_coef = 120.0 + self.jdict["bshin"].power_coef = 90.0 + self.jdict["bfoot"].power_coef = 60.0 + self.jdict["fthigh"].power_coef = 140.0 + self.jdict["fshin"].power_coef = 60.0 + self.jdict["ffoot"].power_coef = 30.0 + +class PybulletAnt(PybulletForwardWalkersBase): + foot_list = ['front_left_foot', 'front_right_foot', 'left_back_foot', 'right_back_foot'] + def __init__(self): + PybulletForwardWalkersBase.__init__(self, "ant.xml", "torso", action_dim=8, obs_dim=28, power=10.5) + def alive_bonus(self, z, pitch): + return +1 if z > 0.26 else -1 # 0.25 is central sphere rad, die if it scrapes the ground + + +## 3d Humanoid ## + +class PybulletHumanoid(PybulletForwardWalkersBase): + self_collision = True + foot_list = ["right_foot", "left_foot"] # "left_hand", "right_hand" + + def __init__(self): + PybulletForwardWalkersBase.__init__(self, 'humanoid_symmetric.xml', 'torso', action_dim=17, obs_dim=44, power=0.41) + # 17 joints, 4 of them important for walking (hip, knee), others may as well be turned off, 17/4 = 4.25 + self.electricity_cost = 4.25*PybulletForwardWalkersBase.electricity_cost + self.stall_torque_cost = 4.25*PybulletForwardWalkersBase.stall_torque_cost + + def robot_specific_reset(self): + PybulletForwardWalkersBase.robot_specific_reset(self) + self.motor_names = ["abdomen_z", "abdomen_y", "abdomen_x"] + self.motor_power = [100, 100, 100] + self.motor_names += ["right_hip_x", "right_hip_z", "right_hip_y", "right_knee"] + self.motor_power += [100, 100, 300, 200] + self.motor_names += ["left_hip_x", "left_hip_z", "left_hip_y", "left_knee"] + self.motor_power += [100, 100, 300, 200] + self.motor_names += ["right_shoulder1", "right_shoulder2", "right_elbow"] + self.motor_power += [75, 75, 75] + self.motor_names += ["left_shoulder1", "left_shoulder2", "left_elbow"] + self.motor_power += [75, 75, 75] + self.motors = [self.jdict[n] for n in self.motor_names] + # if self.random_yaw: # TODO: Make leaning work as soon as the rest works + # cpose = cpp_household.Pose() + # yaw = self.np_random.uniform(low=-3.14, high=3.14) + # if self.random_lean and self.np_random.randint(2)==0: + # cpose.set_xyz(0, 0, 1.4) + # if self.np_random.randint(2)==0: + # pitch = np.pi/2 + # cpose.set_xyz(0, 0, 0.45) + # else: + # pitch = np.pi*3/2 + # cpose.set_xyz(0, 0, 0.25) + # roll = 0 + # cpose.set_rpy(roll, pitch, yaw) + # else: + # cpose.set_xyz(0, 0, 1.4) + # cpose.set_rpy(0, 0, yaw) # just face random direction, but stay straight otherwise + # self.cpp_robot.set_pose_and_speed(cpose, 0,0,0) + self.initial_z = 0.8 + + random_yaw = False + random_lean = False + + def apply_action(self, a): + assert( np.isfinite(a).all() ) + force_gain = 1 + for i, m, power in zip(range(17), self.motors, self.motor_power): + m.set_motor_torque( float(force_gain * power*self.power*a[i]) ) + #m.set_motor_torque(float(force_gain * power * self.power * np.clip(a[i], -1, +1))) + + def alive_bonus(self, z, pitch): + return +2 if z > 0.78 else -1 # 2 here because 17 joints produce a lot of electricity cost just from policy noise, living must be better than dying + diff --git a/examples/pybullet/gym/envs/gym_manipulators.py b/examples/pybullet/gym/envs/gym_manipulators.py new file mode 100644 index 000000000..d42abb511 --- /dev/null +++ b/examples/pybullet/gym/envs/gym_manipulators.py @@ -0,0 +1,304 @@ +from pybulletgym.envs.scene_abstract import SingleRobotEmptyScene +from gym_mujoco_xml_env import PybulletMujocoXmlEnv +import gym, gym.spaces, gym.utils, gym.utils.seeding +import numpy as np +import os, sys + +class PybulletReacher(PybulletMujocoXmlEnv): + def __init__(self): + PybulletMujocoXmlEnv.__init__(self, 'reacher.xml', 'body0', action_dim=2, obs_dim=9) + + def create_single_player_scene(self): + return SingleRobotEmptyScene(gravity=0.0, timestep=0.0165, frame_skip=1) + + TARG_LIMIT = 0.27 + def robot_specific_reset(self): + self.jdict["target_x"].reset_current_position(self.np_random.uniform( low=-self.TARG_LIMIT, high=self.TARG_LIMIT ), 0) + self.jdict["target_y"].reset_current_position(self.np_random.uniform( low=-self.TARG_LIMIT, high=self.TARG_LIMIT ), 0) + self.fingertip = self.parts["fingertip"] + self.target = self.parts["target"] + self.central_joint = self.jdict["joint0"] + self.elbow_joint = self.jdict["joint1"] + self.central_joint.reset_current_position(self.np_random.uniform( low=-3.14, high=3.14 ), 0) + self.elbow_joint.reset_current_position(self.np_random.uniform( low=-3.14, high=3.14 ), 0) + + def apply_action(self, a): + assert( np.isfinite(a).all() ) + self.central_joint.set_motor_torque( 0.05*float(np.clip(a[0], -1, +1)) ) + self.elbow_joint.set_motor_torque( 0.05*float(np.clip(a[1], -1, +1)) ) + + def calc_state(self): + theta, self.theta_dot = self.central_joint.current_relative_position() + self.gamma, self.gamma_dot = self.elbow_joint.current_relative_position() + target_x, _ = self.jdict["target_x"].current_position() + target_y, _ = self.jdict["target_y"].current_position() + self.to_target_vec = np.array(self.fingertip.pose().xyz()) - np.array(self.target.pose().xyz()) + return np.array([ + target_x, + target_y, + self.to_target_vec[0], + self.to_target_vec[1], + np.cos(theta), + np.sin(theta), + self.theta_dot, + self.gamma, + self.gamma_dot, + ]) + + def calc_potential(self): + return -100 * np.linalg.norm(self.to_target_vec) + + def _step(self, a): + assert(not self.scene.multiplayer) + self.apply_action(a) + self.scene.global_step() + + state = self.calc_state() # sets self.to_target_vec + + potential_old = self.potential + self.potential = self.calc_potential() + + electricity_cost = ( + -0.10*(np.abs(a[0]*self.theta_dot) + np.abs(a[1]*self.gamma_dot)) # work torque*angular_velocity + -0.01*(np.abs(a[0]) + np.abs(a[1])) # stall torque require some energy + ) + stuck_joint_cost = -0.1 if np.abs(np.abs(self.gamma)-1) < 0.01 else 0.0 + self.rewards = [float(self.potential - potential_old), float(electricity_cost), float(stuck_joint_cost)] + self.HUD(state, a, False) + return state, sum(self.rewards), False, {} + + def camera_adjust(self): + x, y, z = self.fingertip.pose().xyz() + x *= 0.5 + y *= 0.5 + self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z) + + +class PybulletPusher(PybulletMujocoXmlEnv): + def __init__(self): + PybulletMujocoXmlEnv.__init__(self, 'pusher.xml', 'body0', action_dim=7, obs_dim=5) + + def create_single_player_scene(self): + return SingleRobotEmptyScene(gravity=9.81, timestep=0.0020, frame_skip=5) + + def robot_specific_reset(self): + self.fingertip = self.parts["fingertip"] +# qpos = self.init_qpos + + self.goal_pos = np.asarray([0, 0]) + while True: + self.cylinder_pos = np.concatenate([ + self.np_random.uniform(low=-0.3, high=0, size=1), + self.np_random.uniform(low=-0.2, high=0.2, size=1)]) + if np.linalg.norm(self.cylinder_pos - self.goal_pos) > 0.17: + break + +# This is probably position setting +# qpos[-4:-2] = self.cylinder_pos +# qpos[-2:] = self.goal_pos +# qvel = self.init_qvel + self.np_random.uniform(low=-0.005, +# high=0.005, size=self.model.nv) +# qvel[-4:] = 0 +# self.set_state(qpos, qvel) + + def apply_action(self, a): + assert( np.isfinite(a).all() ) + + def calc_state(self): + return np.concatenate([ + np.array([j.current_position() for j in self.ordered_joints]).flatten(), # position + np.array([j.current_relative_position() for j in self.ordered_joints]).flatten(), # speed + self.parts["fingertip"].pose().xyz(), + self.parts["object"].pose().xyz(), + self.parts["goal"].pose().xyz(), + ]) + + def _step(self, a): + + self.apply_action(a) + self.scene.global_step() + + state = self.calc_state() + + reward_near_vec = self.parts["object"].pose().xyz() - self.parts["fingertip"].pose().xyz() + reward_dist_vec = self.parts["object"].pose().xyz() - self.parts["goal"].pose().xyz() + + reward_near = - np.linalg.norm(reward_near_vec) + reward_dist = - np.linalg.norm(reward_dist_vec) + reward_ctrl = - np.square(a).sum() + reward = reward_dist + 0.1 * reward_ctrl + 0.5 * reward_near + + done = False + return state, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl) + + def camera_adjust(self): + x, y, z = self.fingertip.pose().xyz() + x *= 0.5 + y *= 0.5 + self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z) + + +class PybulletStriker(PybulletMujocoXmlEnv): + def __init__(self): + PybulletMujocoXmlEnv.__init__(self, 'striker.xml', 'body0', action_dim=7, obs_dim=5) + self._striked = False + self._min_strike_dist = np.inf + self.strike_threshold = 0.1 + + def create_single_player_scene(self): + return SingleRobotEmptyScene(gravity=9.81, timestep=0.0020, frame_skip=5) + + def robot_specific_reset(self): + self.fingertip = self.parts["fingertip"] + self._min_strike_dist = np.inf + self._striked = False + self._strike_pos = None + + # reset position of manipulator + for j in self.ordered_joints: + j.reset_current_position(self.np_random.uniform( low=-0.1, high=0.1 )) + + # reset speed of manipulator + + # reset ball position + +# qpos = self.init_qpos + + self.ball = np.array([0.5, -0.175]) + + while True: + self.goal = np.concatenate([ + self.np_random.uniform(low=0.15, high=0.7, size=1), + self.np_random.uniform(low=0.1, high=1.0, size=1)]) + if np.linalg.norm(self.ball - self.goal) > 0.17: + break +# This is probably position setting +# qpos[-9:-7] = [self.ball[1], self.ball[0]] +# qpos[-7:-5] = self.goal +# diff = self.ball - self.goal +# angle = -np.arctan(diff[0] / (diff[1] + 1e-8)) +# qpos[-1] = angle / 3.14 +# qvel = self.init_qvel + self.np_random.uniform(low=-.1, high=.1, +# size=self.model.nv) +# qvel[7:] = 0 +# self.set_state(qpos, qvel) + + def apply_action(self, a): + assert( np.isfinite(a).all() ) + + def calc_state(self): + return np.concatenate([ + np.array([j.current_position() for j in self.ordered_joints]).flatten(), # position + np.array([j.current_relative_position() for j in self.ordered_joints]).flatten(), # speed + self.parts["fingertip"].pose().xyz(), + self.parts["object"].pose().xyz(), + self.parts["goal"].pose().xyz(), + ]) + + def _step(self, a): + self.apply_action(a) + self.scene.global_step() + state = self.calc_state() + + dist_object_finger = self.parts["object"].pose().xyz() - self.parts["fingertip"].pose().xyz() + reward_dist_vec = self.parts["object"].pose().xyz() - self.parts["goal"].pose().xyz() + + self._min_strike_dist = min(self._min_strike_dist, np.linalg.norm(reward_dist_vec)) + + if np.linalg.norm(dist_object_finger) < self.strike_threshold: + self._striked = True + self._strike_pos = self.parts["fingertip"].pose().xyz() + + if self._striked: + reward_near_vec = self.parts["object"].pose().xyz() - self._strike_pos + else: + reward_near_vec = self.parts["object"].pose().xyz() - self.parts["fingertip"].pose().xyz() + + reward_near = - np.linalg.norm(reward_near_vec) + + reward_dist = - np.linalg.norm(self._min_strike_dist) + reward_ctrl = - np.square(a).sum() + reward = 3 * reward_dist + 0.1 * reward_ctrl + 0.5 * reward_near + + done = False + return state, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl) + + def camera_adjust(self): + x, y, z = self.fingertip.pose().xyz() + x *= 0.5 + y *= 0.5 + self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z) + + +class PybulletThrower(PybulletMujocoXmlEnv): + def __init__(self): + PybulletMujocoXmlEnv.__init__(self, 'thrower.xml', 'body0', action_dim=7, obs_dim=5) + self._ball_hit_ground = False + self._ball_hit_location = None + + def create_single_player_scene(self): + return SingleRobotEmptyScene(gravity=0.0, timestep=0.0020, frame_skip=5) + + def robot_specific_reset(self): + self.fingertip = self.parts["fingertip"] + self._ball_hit_ground = False + self._ball_hit_location = None + + # reset position of manipulator + for j in self.ordered_joints: + j.reset_current_position(self.np_random.uniform( low=-0.1, high=0.1 )) + + # reset speed of manipulator +# This is probably position setting +# qpos = self.init_qpos +# self.goal = np.array([self.np_random.uniform(low=-0.3, high=0.3), +# self.np_random.uniform(low=-0.3, high=0.3)]) +# +# qpos[-9:-7] = self.goal +# qvel = self.init_qvel + self.np_random.uniform(low=-0.005, +# high=0.005, size=self.model.nv) +# qvel[7:] = 0 +# self.set_state(qpos, qvel) + + def apply_action(self, a): + assert( np.isfinite(a).all() ) + + def calc_state(self): + return np.concatenate([ + np.array([j.current_position() for j in self.ordered_joints]).flatten(), # position + np.array([j.current_relative_position() for j in self.ordered_joints]).flatten(), # speed + self.parts["fingertip"].pose().xyz(), + self.parts["ball"].pose().xyz(), + self.parts["goal"].pose().xyz(), + ]) + + def _step(self, a): + self.apply_action(a) + self.scene.global_step() + state = self.calc_state() + + ball_xy = self.parts["ball"].pose().xyz()[:2] + goal_xy = self.parts["goal"].pose().xyz()[:2] + + if not self._ball_hit_ground and self.parts["ball"].pose().xyz()[2] < -0.25: + self._ball_hit_ground = True + self._ball_hit_location = self.parts["ball"].pose().xyz() + + if self._ball_hit_ground: + ball_hit_xy = self._ball_hit_location[:2] + reward_dist = -np.linalg.norm(ball_hit_xy - goal_xy) + else: + reward_dist = -np.linalg.norm(ball_xy - goal_xy) + reward_ctrl = - np.square(a).sum() + + reward = reward_dist + 0.002 * reward_ctrl + + done = False + return state, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl) + + def camera_adjust(self): + x, y, z = self.fingertip.pose().xyz() + x *= 0.5 + y *= 0.5 + self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z) + diff --git a/examples/pybullet/gym/envs/gym_mujoco_xml_env.py b/examples/pybullet/gym/envs/gym_mujoco_xml_env.py new file mode 100644 index 000000000..8d412eacc --- /dev/null +++ b/examples/pybullet/gym/envs/gym_mujoco_xml_env.py @@ -0,0 +1,246 @@ +import gym, gym.spaces, gym.utils, gym.utils.seeding +import numpy as np +import pybullet as p +import os + + +class PybulletMujocoXmlEnv(gym.Env): + """ + Base class for MuJoCo .xml actors in a Scene. + These environments create single-player scenes and behave like normal Gym environments, if + you don't use multiplayer. + """ + + metadata = { + 'render.modes': ['human', 'rgb_array'], + 'video.frames_per_second': 60 + } + + self_collision = False + + def __init__(self, model_xml, robot_name, action_dim, obs_dim): + self.scene = None + + self.parts = None + self.jdict = None + self.ordered_joints = None + self.robot_body = None + + high = np.ones([action_dim]) + self.action_space = gym.spaces.Box(-high, high) + high = np.inf*np.ones([obs_dim]) + self.observation_space = gym.spaces.Box(-high, high) + self._seed() + + self.model_xml = model_xml + self.robot_name = robot_name + + self.camera = Camera() + + def _seed(self, seed=None): + self.np_random, seed = gym.utils.seeding.np_random(seed) + return [seed] + + def addToScene(self, bodies): + if self.parts is not None: + parts = self.parts + else: + parts = {} + + if self.jdict is not None: + joints = self.jdict + else: + joints = {} + + if self.ordered_joints is not None: + ordered_joints = self.ordered_joints + else: + ordered_joints = [] + + dump = 0 + for i in range(len(bodies)): + if p.getNumJoints(bodies[i]) == 0: + part_name, robot_name = p.getBodyInfo(bodies[i], 0) + robot_name = robot_name.decode("utf8") + part_name = part_name.decode("utf8") + parts[part_name] = BodyPart(part_name, bodies, i, -1) + for j in range(p.getNumJoints(bodies[i])): + _,joint_name,_,_,_,_,_,_,_,_,_,_,part_name = p.getJointInfo(bodies[i], j) + + joint_name = joint_name.decode("utf8") + part_name = part_name.decode("utf8") + + if dump: print("ROBOT PART '%s'" % part_name) + if dump: print("ROBOT JOINT '%s'" % joint_name) # limits = %+0.2f..%+0.2f effort=%0.3f speed=%0.3f" % ((joint_name,) + j.limits()) ) + + parts[part_name] = BodyPart(part_name, bodies, i, j) + + if part_name == self.robot_name: + self.robot_body = parts[part_name] + + if i == 0 and j == 0 and self.robot_body is None: # if nothing else works, we take this as robot_body + parts[self.robot_name] = BodyPart(self.robot_name, bodies, 0, -1) + self.robot_body = parts[self.robot_name] + + if joint_name[:6] == "ignore": + Joint(joint_name, bodies, i, j).disable_motor() + continue + + if joint_name[:8] != "jointfix": + joints[joint_name] = Joint(joint_name, bodies, i, j) + ordered_joints.append(joints[joint_name]) + + joints[joint_name].power_coef = 100.0 + + return parts, joints, ordered_joints, self.robot_body + + def _reset(self): + if self.scene is None: + self.scene = self.create_single_player_scene() + if not self.scene.multiplayer: + self.scene.episode_restart() + + self.ordered_joints = [] + self.frame = 0 + self.done = 0 + self.reward = 0 + dump = 0 + + if self.self_collision: + self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(p.loadMJCF(os.path.join(os.path.dirname(__file__), "mujoco_assets", self.model_xml), flags = p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS)) + else: + self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(p.loadMJCF(os.path.join(os.path.dirname(__file__), "mujoco_assets", self.model_xml))) + + self.robot_specific_reset() + s = self.calc_state() # optimization: calc_state() can calculate something in self.* for calc_potential() to use + self.potential = self.calc_potential() + return s + + def _render(self, mode, close): + pass + + def calc_potential(self): + return 0 + + def HUD(self, state, a, done): + pass + +class Camera: + def __init__(self): + pass + + def move_and_look_at(self,i,j,k,x,y,z): + lookat = [x,y,z] + distance = 10 + yaw = 10 + p.resetDebugVisualizerCamera(distance, yaw, -20, lookat) + +class Pose_Helper: # dummy class to comply to original interface + def __init__(self, body_part): + self.body_part = body_part + + def xyz(self): + return self.body_part.current_position() + + def rpy(self): + return p.getEulerFromQuaternion(self.body_part.current_orientation()) + + def orientation(self): + return self.body_part.current_orientation() + +class BodyPart: + def __init__(self, body_name, bodies, bodyIndex, bodyPartIndex): + self.bodies = bodies + self.bodyIndex = bodyIndex + self.bodyPartIndex = bodyPartIndex + self.initialPosition = self.current_position() + self.initialOrientation = self.current_orientation() + self.bp_pose = Pose_Helper(self) + + def state_fields_of_pose_of(self, body_id, link_id=-1): # a method you will most probably need a lot to get pose and orientation + if link_id == -1: + (x, y, z), (a, b, c, d) = p.getBasePositionAndOrientation(body_id) + else: + (x, y, z), (a, b, c, d), _, _, _, _ = p.getLinkState(body_id, link_id) + return np.array([x, y, z, a, b, c, d]) + + def get_pose(self): + return self.state_fields_of_pose_of(self.bodies[self.bodyIndex], self.bodyPartIndex) + + def speed(self): + if self.bodyPartIndex == -1: + (vx, vy, vz), _ = p.getBaseVelocity(self.bodies[self.bodyIndex]) + else: + (x,y,z), (a,b,c,d), _,_,_,_, (vx, vy, vz), (vr,vp,vy) = p.getLinkState(self.bodies[self.bodyIndex], self.bodyPartIndex, computeLinkVelocity=1) + return np.array([vx, vy, vz]) + + def current_position(self): + return self.get_pose()[:3] + + def current_orientation(self): + return self.get_pose()[3:] + + def reset_position(self, position): + p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], position, self.get_orientation()) + + def reset_orientation(self, orientation): + p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], self.get_position(), orientation) + + def reset_pose(self, position, orientation): + p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], position, orientation) + + def pose(self): + return self.bp_pose + + def contact_list(self): + return p.getContactPoints(self.bodies[self.bodyIndex], -1, self.bodyPartIndex, -1) + + +class Joint: + def __init__(self, joint_name, bodies, bodyIndex, jointIndex): + self.bodies = bodies + self.bodyIndex = bodyIndex + self.jointIndex = jointIndex + self.joint_name = joint_name + _,_,_,_,_,_,_,_,self.lowerLimit, self.upperLimit,_,_,_ = p.getJointInfo(self.bodies[self.bodyIndex], self.jointIndex) + self.power_coeff = 0 + + def set_state(self, x, vx): + p.resetJointState(self.bodies[self.bodyIndex], self.jointIndex, x, vx) + + def current_position(self): # just some synonyme method + return self.get_state() + + def current_relative_position(self): + pos, vel = self.get_state() + pos_mid = 0.5 * (self.lowerLimit + self.upperLimit); + return ( + 2 * (pos - pos_mid) / (self.upperLimit - self.lowerLimit), + 0.1 * vel + ) + + def get_state(self): + x, vx,_,_ = p.getJointState(self.bodies[self.bodyIndex],self.jointIndex) + return x, vx + + def set_position(self, position): + p.setJointMotorControl2(self.bodies[self.bodyIndex],self.jointIndex,p.POSITION_CONTROL, targetPosition=position) + + def set_velocity(self, velocity): + p.setJointMotorControl2(self.bodies[self.bodyIndex],self.jointIndex,p.VELOCITY_CONTROL, targetVelocity=velocity) + + def set_motor_torque(self, torque): # just some synonyme method + self.set_torque(torque) + + def set_torque(self, torque): + p.setJointMotorControl2(bodyIndex=self.bodies[self.bodyIndex], jointIndex=self.jointIndex, controlMode=p.TORQUE_CONTROL, force=torque) #, positionGain=0.1, velocityGain=0.1) + + def reset_current_position(self, position, velocity): # just some synonyme method + self.reset_position(position, velocity) + + def reset_position(self, position, velocity): + p.resetJointState(self.bodies[self.bodyIndex],self.jointIndex,targetValue=position, targetVelocity=velocity) + self.disable_motor() + + def disable_motor(self): + p.setJointMotorControl2(self.bodies[self.bodyIndex],self.jointIndex,controlMode=p.VELOCITY_CONTROL, force=0) diff --git a/examples/pybullet/gym/envs/gym_pendula.py b/examples/pybullet/gym/envs/gym_pendula.py new file mode 100644 index 000000000..c13230e06 --- /dev/null +++ b/examples/pybullet/gym/envs/gym_pendula.py @@ -0,0 +1,133 @@ +from pybulletgym.envs.scene_abstract import SingleRobotEmptyScene +from gym_mujoco_xml_env import PybulletMujocoXmlEnv +import gym, gym.spaces, gym.utils, gym.utils.seeding +import numpy as np +import os, sys + +class PybulletInvertedPendulum(PybulletMujocoXmlEnv): + swingup = False + force_gain = 12 # TODO: Try to find out why we need to scale the force + + def __init__(self): + PybulletMujocoXmlEnv.__init__(self, 'inverted_pendulum.xml', 'cart', action_dim=1, obs_dim=5) + + def create_single_player_scene(self): + return SingleRobotEmptyScene(gravity=9.8, timestep=0.0165, frame_skip=1) + + def robot_specific_reset(self): + self.pole = self.parts["pole"] + self.slider = self.jdict["slider"] + self.j1 = self.jdict["hinge"] + u = self.np_random.uniform(low=-.1, high=.1) + self.j1.reset_current_position( u if not self.swingup else 3.1415+u , 0) + self.j1.set_motor_torque(0) + + def apply_action(self, a): + #assert( np.isfinite(a).all() ) + if not np.isfinite(a).all(): + print("a is inf") + a[0] = 0 + self.slider.set_motor_torque( self.force_gain * 100*float(np.clip(a[0], -1, +1)) ) + + def calc_state(self): + self.theta, theta_dot = self.j1.current_position() + x, vx = self.slider.current_position() + #assert( np.isfinite(x) ) + + if not np.isfinite(x): + print("x is inf") + x = 0 + + if not np.isfinite(vx): + print("vx is inf") + vx = 0 + + if not np.isfinite(self.theta): + print("theta is inf") + self.theta = 0 + + if not np.isfinite(theta_dot): + print("theta_dot is inf") + theta_dot = 0 + + return np.array([ + x, vx, + np.cos(self.theta), np.sin(self.theta), theta_dot + ]) + + def _step(self, a): + self.apply_action(a) + self.scene.global_step() + state = self.calc_state() # sets self.pos_x self.pos_y + vel_penalty = 0 + if self.swingup: + reward = np.cos(self.theta) + done = False + else: + reward = 1.0 + done = np.abs(self.theta) > .2 + self.rewards = [float(reward)] + self.HUD(state, a, done) + return state, sum(self.rewards), done, {} + + def camera_adjust(self): + self.camera.move_and_look_at(0,1.2,1.0, 0,0,0.5) + +class PybulletInvertedPendulumSwingup(PybulletInvertedPendulum): + swingup = True + force_gain = 2.2 # TODO: Try to find out why we need to scale the force + +class PybulletInvertedDoublePendulum(PybulletMujocoXmlEnv): + def __init__(self): + PybulletMujocoXmlEnv.__init__(self, 'inverted_double_pendulum.xml', 'cart', action_dim=1, obs_dim=9) + + def create_single_player_scene(self): + return SingleRobotEmptyScene(gravity=9.8, timestep=0.0165, frame_skip=1) + + def robot_specific_reset(self): + self.pole2 = self.parts["pole2"] + self.slider = self.jdict["slider"] + self.j1 = self.jdict["hinge"] + self.j2 = self.jdict["hinge2"] + u = self.np_random.uniform(low=-.1, high=.1, size=[2]) + self.j1.reset_current_position(float(u[0]), 0) + self.j2.reset_current_position(float(u[1]), 0) + self.j1.set_motor_torque(0) + self.j2.set_motor_torque(0) + + def apply_action(self, a): + assert( np.isfinite(a).all() ) + force_gain = 0.78 # TODO: Try to find out why we need to scale the force + self.slider.set_motor_torque( force_gain *200*float(np.clip(a[0], -1, +1)) ) + + def calc_state(self): + theta, theta_dot = self.j1.current_position() + gamma, gamma_dot = self.j2.current_position() + x, vx = self.slider.current_position() + self.pos_x, _, self.pos_y = self.pole2.pose().xyz() + assert( np.isfinite(x) ) + return np.array([ + x, vx, + self.pos_x, + np.cos(theta), np.sin(theta), theta_dot, + np.cos(gamma), np.sin(gamma), gamma_dot, + ]) + + def _step(self, a): + self.apply_action(a) + self.scene.global_step() + state = self.calc_state() # sets self.pos_x self.pos_y + # upright position: 0.6 (one pole) + 0.6 (second pole) * 0.5 (middle of second pole) = 0.9 + # using tag in original xml, upright position is 0.6 + 0.6 = 1.2, difference +0.3 + dist_penalty = 0.01 * self.pos_x ** 2 + (self.pos_y + 0.3 - 2) ** 2 + # v1, v2 = self.model.data.qvel[1:3] TODO when this fixed https://github.com/bulletphysics/bullet3/issues/1040 + #vel_penalty = 1e-3 * v1**2 + 5e-3 * v2**2 + vel_penalty = 0 + alive_bonus = 10 + done = self.pos_y + 0.3 <= 1 + self.rewards = [float(alive_bonus), float(-dist_penalty), float(-vel_penalty)] + self.HUD(state, a, done) + return state, sum(self.rewards), done, {} + + def camera_adjust(self): + self.camera.move_and_look_at(0,1.2,1.2, 0,0,0.5) diff --git a/examples/pybullet/gym/envs/kerasrl_utils.py b/examples/pybullet/gym/envs/kerasrl_utils.py new file mode 100644 index 000000000..1ccedce5c --- /dev/null +++ b/examples/pybullet/gym/envs/kerasrl_utils.py @@ -0,0 +1,28 @@ + +import re +from gym import error +import glob + # checkpoints/KerasDDPG-InvertedPendulum-v0-20170701190920_actor.h5 +weight_save_re = re.compile(r'^(?:\w+\/)+?(\w+-v\d+)-(\w+-v\d+)-(\d+)(?:_\w+)?\.(\w+)$') + +def get_fields(weight_save_name): + match = weight_save_re.search(weight_save_name) + if not match: + raise error.Error('Attempted to read a malformed weight save: {}. (Currently all weight saves must be of the form {}.)'.format(id,weight_save_re.pattern)) + return match.group(1), match.group(2), int(match.group(3)) + +def get_latest_save(file_folder, agent_name, env_name, version_number): + """ + Returns the properties of the latest weight save. The information can be used to generate the loading path + :return: + """ + path = "%s%s"% (file_folder, "*.h5") + file_list = glob.glob(path) + latest_file_properties = [] + file_properties = [] + for f in file_list: + file_properties = get_fields(f) + if file_properties[0] == agent_name and file_properties[1] == env_name and (latest_file_properties == [] or file_properties[2] > latest_file_properties[2]): + latest_file_properties = file_properties + + return latest_file_properties diff --git a/examples/pybullet/gym/envs/mujoco_assets/ant.xml b/examples/pybullet/gym/envs/mujoco_assets/ant.xml new file mode 100644 index 000000000..311d96feb --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/ant.xml @@ -0,0 +1,71 @@ + + + diff --git a/examples/pybullet/gym/envs/mujoco_assets/ground_plane.xml b/examples/pybullet/gym/envs/mujoco_assets/ground_plane.xml new file mode 100644 index 000000000..0212c7f1b --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/ground_plane.xml @@ -0,0 +1,5 @@ + + + + + diff --git a/examples/pybullet/gym/envs/mujoco_assets/half_cheetah.xml b/examples/pybullet/gym/envs/mujoco_assets/half_cheetah.xml new file mode 100644 index 000000000..418eef56d --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/half_cheetah.xml @@ -0,0 +1,88 @@ + + + + + + + + + + diff --git a/examples/pybullet/gym/envs/mujoco_assets/hopper.xml b/examples/pybullet/gym/envs/mujoco_assets/hopper.xml new file mode 100644 index 000000000..077160345 --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/hopper.xml @@ -0,0 +1,39 @@ + + + + + + + + diff --git a/examples/pybullet/gym/envs/mujoco_assets/humanoid_symmetric.xml b/examples/pybullet/gym/envs/mujoco_assets/humanoid_symmetric.xml new file mode 100644 index 000000000..d7ba9f494 --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/humanoid_symmetric.xml @@ -0,0 +1,107 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/examples/pybullet/gym/envs/mujoco_assets/humanoid_symmetric_no_ground.xml b/examples/pybullet/gym/envs/mujoco_assets/humanoid_symmetric_no_ground.xml new file mode 100644 index 000000000..0c2f2befa --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/humanoid_symmetric_no_ground.xml @@ -0,0 +1,115 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/examples/pybullet/gym/envs/mujoco_assets/inverted_double_pendulum.xml b/examples/pybullet/gym/envs/mujoco_assets/inverted_double_pendulum.xml new file mode 100644 index 000000000..755cefd44 --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/inverted_double_pendulum.xml @@ -0,0 +1,47 @@ + + + + + + + + + + + diff --git a/examples/pybullet/gym/envs/mujoco_assets/inverted_pendulum.xml b/examples/pybullet/gym/envs/mujoco_assets/inverted_pendulum.xml new file mode 100644 index 000000000..b409b3b11 --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/inverted_pendulum.xml @@ -0,0 +1,27 @@ + + + + + + + + + diff --git a/examples/pybullet/gym/envs/mujoco_assets/pusher.xml b/examples/pybullet/gym/envs/mujoco_assets/pusher.xml new file mode 100644 index 000000000..62abc06a9 --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/pusher.xml @@ -0,0 +1,91 @@ + + + diff --git a/examples/pybullet/gym/envs/mujoco_assets/reacher.xml b/examples/pybullet/gym/envs/mujoco_assets/reacher.xml new file mode 100644 index 000000000..82b224ff6 --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/reacher.xml @@ -0,0 +1,39 @@ + + + + + + + diff --git a/examples/pybullet/gym/envs/mujoco_assets/striker.xml b/examples/pybullet/gym/envs/mujoco_assets/striker.xml new file mode 100644 index 000000000..75602324a --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/striker.xml @@ -0,0 +1,101 @@ + + + diff --git a/examples/pybullet/gym/envs/mujoco_assets/thrower.xml b/examples/pybullet/gym/envs/mujoco_assets/thrower.xml new file mode 100644 index 000000000..0ca6c3ea1 --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/thrower.xml @@ -0,0 +1,127 @@ + + + diff --git a/examples/pybullet/gym/envs/mujoco_assets/walker2d.xml b/examples/pybullet/gym/envs/mujoco_assets/walker2d.xml new file mode 100644 index 000000000..f1905c067 --- /dev/null +++ b/examples/pybullet/gym/envs/mujoco_assets/walker2d.xml @@ -0,0 +1,52 @@ + + + + + + + diff --git a/examples/pybullet/gym/envs/other_assets/roboschool/models_outdoor/stadium/part0.obj b/examples/pybullet/gym/envs/other_assets/roboschool/models_outdoor/stadium/part0.obj new file mode 100644 index 000000000..0fa502c62 --- /dev/null +++ b/examples/pybullet/gym/envs/other_assets/roboschool/models_outdoor/stadium/part0.obj @@ -0,0 +1,23154 @@ +# 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