mirror of
https://github.com/bulletphysics/bullet3
synced 2025-01-07 08:10:08 +00:00
112 lines
3.0 KiB
Python
112 lines
3.0 KiB
Python
import os, inspect
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currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
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parentdir = os.path.dirname(os.path.dirname(currentdir))
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os.sys.path.insert(0,parentdir)
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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 . import simpleHumanoid
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import random
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import pybullet_data
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class SimpleHumanoidGymEnv(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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urdfRoot=pybullet_data.getDataPath(),
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actionRepeat=50,
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isEnableSelfCollision=True,
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renders=True):
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print("init")
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self._timeStep = 0.01
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self._urdfRoot = urdfRoot
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self._actionRepeat = actionRepeat
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self._isEnableSelfCollision = isEnableSelfCollision
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self._observation = []
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self._envStepCounter = 0
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self._renders = renders
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self._p = p
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if self._renders:
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p.connect(p.GUI)
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else:
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p.connect(p.DIRECT)
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self._seed()
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self.reset()
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observationDim = len(self.getExtendedObservation())
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#print("observationDim")
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#print(observationDim)
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observation_high = np.array([np.finfo(np.float32).max] * observationDim)
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self.action_space = spaces.Discrete(9)
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self.observation_space = spaces.Box(-observation_high, observation_high)
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self.viewer = None
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def _reset(self):
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p.resetSimulation()
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#p.setPhysicsEngineParameter(numSolverIterations=300)
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p.setTimeStep(self._timeStep)
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p.loadURDF(os.path.join(self._urdfRoot,"plane.urdf"))
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dist = 5 +2.*random.random()
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ang = 2.*3.1415925438*random.random()
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ballx = dist * math.sin(ang)
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bally = dist * math.cos(ang)
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ballz = 1
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p.setGravity(0,0,-10)
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self._humanoid = simpleHumanoid.SimpleHumanoid(urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
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self._envStepCounter = 0
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p.stepSimulation()
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self._observation = self.getExtendedObservation()
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return np.array(self._observation)
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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 getExtendedObservation(self):
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self._observation = self._humanoid.getObservation()
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return self._observation
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def _step(self, action):
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self._humanoid.applyAction(action)
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for i in range(self._actionRepeat):
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p.stepSimulation()
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if self._renders:
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time.sleep(self._timeStep)
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self._observation = self.getExtendedObservation()
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if self._termination():
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break
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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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#print("len=%r" % len(self._observation))
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return np.array(self._observation), reward, done, {}
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def _render(self, mode='human', close=False):
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return
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def _termination(self):
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return self._envStepCounter>1000
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def _reward(self):
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reward=self._humanoid.distance
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print(reward)
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return reward
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