prepare to train racecar using ZED camera pixels (CNN+DQN)

examples/SharedMemory/PhysicsClientExample.cpp

@@ -863,8 +863,8 @@ void	PhysicsClientExample::stepSimulation(float deltaTime)
 						{
                             int xIndex = int(float(i)*(float(imageData.m_pixelWidth)/float(camVisualizerWidth)));
                             int yIndex = int(float(j)*(float(imageData.m_pixelHeight)/float(camVisualizerHeight)));
-							btClamp(yIndex,0,imageData.m_pixelHeight);
 							btClamp(xIndex,0,imageData.m_pixelWidth);
+							btClamp(yIndex,0,imageData.m_pixelHeight);
 							
                             if (m_canvasDepthIndex >=0)
                             {

examples/SharedMemory/PhysicsServerExample.cpp

@@ -1878,8 +1878,8 @@ void	PhysicsServerExample::updateGraphics()
 						{
                             int xIndex = int(float(i)*(float(m_multiThreadedHelper->m_destinationWidth)/float(gCamVisualizerWidth)));
                             int yIndex = int(float(j)*(float(m_multiThreadedHelper->m_destinationHeight)/float(gCamVisualizerHeight)));
-							btClamp(yIndex,0,m_multiThreadedHelper->m_destinationWidth);
-							btClamp(xIndex,0,m_multiThreadedHelper->m_destinationHeight);
+							btClamp(xIndex,0,m_multiThreadedHelper->m_destinationWidth);
+							btClamp(yIndex,0,m_multiThreadedHelper->m_destinationHeight);
 							int bytesPerPixel = 4; //RGBA
 							
 							if (m_canvasRGBIndex >=0)

examples/pybullet/gym/enjoy_pybullet_zed_racecar.py

@@ -0,0 +1,26 @@
+import gym
+from envs.bullet.racecarZEDGymEnv import RacecarZEDGymEnv
+
+from baselines import deepq
+
+
+def main():
+    
+    env = RacecarZEDGymEnv(renders=True)
+    act = deepq.load("racecar_model.pkl")
+    print(act)
+    while True:
+        obs, done = env.reset(), False
+        print("===================================")        
+        print("obs")
+        print(obs)
+        episode_rew = 0
+        while not done:
+            env.render()
+            obs, rew, done, _ = env.step(act(obs[None])[0])
+            episode_rew += rew
+        print("Episode reward", episode_rew)
+
+
+if __name__ == '__main__':
+    main()

examples/pybullet/gym/envs/__init__.py

@@ -22,3 +22,10 @@ register(
     timestep_limit=1000,
     reward_threshold=5.0,
 )
+
+register(
+    id='RacecarZedBulletEnv-v0',
+    entry_point='envs.bullet:RacecarZEDGymEnv',
+    timestep_limit=1000,
+    reward_threshold=5.0,
+)

examples/pybullet/gym/envs/bullet/racecarZEDGymEnv.py

@@ -0,0 +1,154 @@
+import math
+import gym
+from gym import spaces
+from gym.utils import seeding
+import numpy as np
+import time
+import pybullet as p
+from . import racecar
+import random
+
+class RacecarZEDGymEnv(gym.Env):
+  metadata = {
+      'render.modes': ['human', 'rgb_array'],
+      'video.frames_per_second' : 50
+  }
+
+  def __init__(self,
+               urdfRoot="",
+               actionRepeat=50,
+               isEnableSelfCollision=True,
+               renders=True):
+    print("init")
+    self._timeStep = 0.01
+    self._urdfRoot = urdfRoot
+    self._actionRepeat = actionRepeat
+    self._isEnableSelfCollision = isEnableSelfCollision
+    self._observation = []
+    self._ballUniqueId = -1
+    self._envStepCounter = 0
+    self._renders = renders
+    self._p = p
+    if self._renders:
+      p.connect(p.GUI)
+    else:
+      p.connect(p.DIRECT)
+    self._seed()
+    self.reset()
+    observationDim = len(self.getExtendedObservation())
+    #print("observationDim")
+    #print(observationDim)
+    
+    observation_high = np.array([np.finfo(np.float32).max] * observationDim)    
+    self.action_space = spaces.Discrete(6)
+    self.observation_space = spaces.Box(-observation_high, observation_high)
+    self.viewer = None
+
+  def _reset(self):
+    p.resetSimulation()
+    #p.setPhysicsEngineParameter(numSolverIterations=300)
+    p.setTimeStep(self._timeStep)
+    #p.loadURDF("%splane.urdf" % self._urdfRoot)
+    stadiumobjects = p.loadSDF("%sstadium.sdf" % self._urdfRoot)
+    #move the stadium objects slightly above 0
+    for i in stadiumobjects:
+      pos,orn = p.getBasePositionAndOrientation(i)
+      newpos = [pos[0],pos[1],pos[2]+0.1]
+      p.resetBasePositionAndOrientation(i,newpos,orn)
+    
+    dist = 5 +2.*random.random()
+    ang = 2.*3.1415925438*random.random()
+    
+    ballx = dist * math.sin(ang)
+    bally = dist * math.cos(ang)
+    ballz = 1
+    
+    self._ballUniqueId = p.loadURDF("sphere2.urdf",[ballx,bally,ballz])
+    p.setGravity(0,0,-10)
+    self._racecar = racecar.Racecar(urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
+    self._envStepCounter = 0
+    for i in range(100):
+      p.stepSimulation()
+    self._observation = self.getExtendedObservation()
+    return np.array(self._observation)
+
+  def __del__(self):
+    p.disconnect()
+
+  def _seed(self, seed=None):
+    self.np_random, seed = seeding.np_random(seed)
+    return [seed]
+
+  def getExtendedObservation(self):
+     self._observation = [] #self._racecar.getObservation()
+     carpos,carorn = p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
+     carmat = p.getMatrixFromQuaternion(carorn)
+     ballpos,ballorn = p.getBasePositionAndOrientation(self._ballUniqueId)      
+     invCarPos,invCarOrn = p.invertTransform(carpos,carorn)
+     ballPosInCar,ballOrnInCar = p.multiplyTransforms(invCarPos,invCarOrn,ballpos,ballorn)
+     dist0 = 0.3
+     dist1 = 1.
+     if self._renders:
+       print("carpos")
+       print(carpos)
+       eyePos = carpos
+       print("carmat012")
+       print(carmat[0],carmat[1],carmat[2])
+       print("carmat345")
+       print(carmat[3],carmat[4],carmat[5])
+       print("carmat678")
+       print(carmat[6],carmat[7],carmat[8])
+       eyePos = [carpos[0]+dist0*carmat[0],carpos[1]+dist0*carmat[3],carpos[2]+dist0*carmat[6]+0.3]
+       targetPos = [carpos[0]+dist1*carmat[0],carpos[1]+dist1*carmat[3],carpos[2]+dist1*carmat[6]+0.3]
+      
+       up = [0,0,1]
+       viewMat = p.computeViewMatrix(eyePos,targetPos,up)
+       #viewMat = p.computeViewMatrixFromYawPitchRoll(carpos,1,0,0,0,2)
+       p.getCameraImage(width=320,height=240,viewMatrix=viewMat,projectionMatrix=p.getDebugVisualizerCamera()[3],renderer=p.ER_BULLET_HARDWARE_OPENGL)
+
+     self._observation.extend([ballPosInCar[0],ballPosInCar[1]])
+     return self._observation
+     
+  def _step(self, action):
+    if (self._renders):
+      basePos,orn = p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
+      #p.resetDebugVisualizerCamera(1, 30, -40, basePos)
+    
+    fwd = [5,0,5,10,10,10]
+    steerings = [-0.5,0,0.5,-0.3,0,0.3]
+    forward = fwd[action]
+    steer = steerings[action]
+    realaction = [forward,steer]
+    self._racecar.applyAction(realaction)
+    for i in range(self._actionRepeat):
+      p.stepSimulation()
+      if self._renders:
+        time.sleep(self._timeStep)
+      self._observation = self.getExtendedObservation()
+      
+      if self._termination():
+        break
+      self._envStepCounter += 1
+    reward = self._reward()
+    done = self._termination()
+    #print("len=%r" % len(self._observation))
+    
+    return np.array(self._observation), reward, done, {}
+
+  def _render(self, mode='human', close=False):
+      return
+
+  def _termination(self):
+    return self._envStepCounter>1000
+    
+  def _reward(self):
+    closestPoints = p.getClosestPoints(self._racecar.racecarUniqueId,self._ballUniqueId,10000) 
+    
+    numPt = len(closestPoints)
+    reward=-1000
+    #print(numPt)
+    if (numPt>0):
+      #print("reward:")
+      reward = -closestPoints[0][8]
+      #print(reward)
+    return reward

examples/pybullet/gym/train_pybullet_zed_racecar.py

@@ -0,0 +1,38 @@
+import gym
+from envs.bullet.racecarZEDGymEnv import RacecarZEDGymEnv
+
+from baselines import deepq
+
+import datetime
+
+
+
+def callback(lcl, glb):
+    # stop training if reward exceeds 199
+    total = sum(lcl['episode_rewards'][-101:-1]) / 100
+    totalt = lcl['t']
+    is_solved = totalt > 2000 and total >= -50
+    return is_solved
+
+
+def main():
+  
+    env = RacecarZEDGymEnv(renders=False)
+    model = deepq.models.mlp([64])
+    act = deepq.learn(
+        env,
+        q_func=model,
+        lr=1e-3,
+        max_timesteps=10000,
+        buffer_size=50000,
+        exploration_fraction=0.1,
+        exploration_final_eps=0.02,
+        print_freq=10,
+        callback=callback
+    )
+    print("Saving model to racecar_model.pkl")
+    act.save("racecar_model.pkl")
+
+
+if __name__ == '__main__':
+    main()