126 lines
3.5 KiB
Python
126 lines
3.5 KiB
Python
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 RacecarGymEnv(gym.Env):
|
|
metadata = {
|
|
'render.modes': ['human', 'rgb_array'],
|
|
'video.frames_per_second' : 50
|
|
}
|
|
|
|
def __init__(self,
|
|
urdfRoot="",
|
|
actionRepeat=1,
|
|
isEnableSelfCollision=True,
|
|
render=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._render = render
|
|
self._p = p
|
|
if self._render:
|
|
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(9)
|
|
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)
|
|
p.loadSDF("%sstadium.sdf" % self._urdfRoot)
|
|
|
|
dist = 1.+10.*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()
|
|
pos,orn = p.getBasePositionAndOrientation(self._ballUniqueId)
|
|
self._observation.extend(list(pos))
|
|
return self._observation
|
|
|
|
def _step(self, action):
|
|
if (self._render):
|
|
basePos,orn = p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
|
|
#p.resetDebugVisualizerCamera(1, 30, -40, basePos)
|
|
|
|
fwd = [-1,-1,-1,0,0,0,1,1,1]
|
|
steerings = [-0.5,0,0.5,-0.5,0,0.5,-0.5,0,0.5]
|
|
forward = fwd[action]
|
|
steer = steerings[action]
|
|
realaction = [forward,steer]
|
|
self._racecar.applyAction(realaction)
|
|
for i in range(self._actionRepeat):
|
|
p.stepSimulation()
|
|
if self._render:
|
|
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
|