21f9d1b816
work-in-progress (need to add missing data files, fix paths etc)
example:
pip install pybullet
pip install gym
python
import gym
import pybullet
import pybullet_envs
env = gym.make("HumanoidBulletEnv-v0")
91 lines
2.6 KiB
Python
91 lines
2.6 KiB
Python
import math
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import gym
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from gym import spaces
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from gym.utils import seeding
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import numpy as np
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import time
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import pybullet as p
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from pybullet_envs.bullet.minitaur import Minitaur
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class MinitaurBulletEnv(gym.Env):
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metadata = {
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'render.modes': ['human', 'rgb_array'],
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'video.frames_per_second' : 50
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}
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def __init__(self):
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self._timeStep = 0.01
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self._observation = []
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self._envStepCounter = 0
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self._lastBasePosition = [0, 0, 0]
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p.connect(p.GUI)
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p.resetSimulation()
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p.setTimeStep(self._timeStep)
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p.loadURDF("plane.urdf")
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p.setGravity(0,0,-10)
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self._minitaur = Minitaur()
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observationDim = self._minitaur.getObservationDimension()
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observation_high = np.array([np.finfo(np.float32).max] * observationDim)
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actionDim = 8
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action_high = np.array([math.pi / 2.0] * actionDim)
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self.action_space = spaces.Box(-action_high, action_high)
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self.observation_space = spaces.Box(-observation_high, observation_high)
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self._seed()
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self.reset()
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self.viewer = None
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def __del__(self):
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p.disconnect()
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def _seed(self, seed=None):
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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def _step(self, action):
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if len(action) != self._minitaur.getActionDimension():
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raise ValueError("We expect {} continuous action not {}.".format(self._minitaur.getActionDimension(), len(actions.continuous_actions)))
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for i in range(len(action)):
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if not -math.pi/2 <= action[i] <= math.pi/2:
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raise ValueError("{}th action should be between -1 and 1 not {}.".format(i, action[i]))
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action[i] += math.pi / 2
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self._minitaur.applyAction(action)
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p.stepSimulation()
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self._observation = self._minitaur.getObservation()
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self._envStepCounter += 1
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reward = self._reward()
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done = self._termination()
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return np.array(self._observation), reward, done, {}
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def _reset(self):
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p.resetSimulation()
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p.setTimeStep(self._timeStep)
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p.loadURDF("plane.urdf")
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p.setGravity(0,0,-10)
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self._minitaur = Minitaur()
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self._observation = self._minitaur.getObservation()
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def _render(self, mode='human', close=False):
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return
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def is_fallen(self):
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orientation = self._minitaur.getBaseOrientation()
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rotMat = p.getMatrixFromQuaternion(orientation)
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localUp = rotMat[6:]
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return np.dot(np.asarray([0, 0, 1]), np.asarray(localUp)) < 0
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def _termination(self):
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return self.is_fallen()
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def _reward(self):
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currentBasePosition = self._minitaur.getBasePosition()
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reward = np.dot(np.asarray([-1, 0, 0]), np.asarray(currentBasePosition) - np.asarray(self._lastBasePosition))
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self._lastBasePosition = currentBasePosition
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return reward
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