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gym_torcs.py
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gym_torcs.py
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import gym
from gym import spaces
import numpy as np
import snakeoil3_gym as snakeoil3
import numpy as np
import copy
import collections as col
import os
import time
import logging
#try
class TorcsEnv:
terminal_judge_start = 100 # If after 100 timestep still no progress, terminated
termination_limit_progress = 5 # [km/h], episode terminates if car is running slower than this limit
default_speed = 50
initial_reset = True
def __init__(self, vision=False, throttle=False, gear_change=False, track_name='practice.xml'):
self.track_name = track_name
self.vision = vision
self.throttle = throttle
self.gear_change = gear_change
self.initial_run = True
os.system('pkill torcs')
time.sleep(0.5)
logging.info('Init TORCS Environment')
if self.vision is True:
config_string = 'torcs -s -r /usr/local/share/games/torcs/config/raceman/' + self.track_name + ' -nofuel -nodamage -nolaptime -vision &'
os.system(config_string)
else:
#config_string = 'torcs -s -r /usr/local/share/games/torcs/config/raceman/' + self.track_name + ' -T -nofuel &'
os.system('torcs -s -r /usr/local/share/games/torcs/config/raceman/' + self.track_name + ' -T -nofuel -nodamage -nolaptime &')
#os.system('torcs -T -nofuel &')
time.sleep(0.5)
os.system('sh autostart.sh')
time.sleep(0.5)
#os.system(config_string)
"""
# Modify here if you use multiple tracks in the environment
self.client = snakeoil3.Client(p=3101, vision=self.vision) # Open new UDP in vtorcs
self.client.MAX_STEPS = np.inf
client = self.client
client.get_servers_input() # Get the initial input from torcs
obs = client.S.d # Get the current full-observation from torcs
"""
if throttle is False:
self.action_space = spaces.Box(low=-1.0, high=1.0, shape=(1,))
else:
self.action_space = spaces.Box( np.array([-1, 0, 0]), np.array([1, 1, 1]), dtype=np.float32)
high = np.array([np.inf for _ in range(30)])
self.observation_space = spaces.Box(low=-high, high=high)
#if vision is False:
# high = np.array([1., np.inf, np.inf, np.inf, 1., np.inf, 1., np.inf])
# low = np.array([0., -np.inf, -np.inf, -np.inf, 0., -np.inf, 0., -np.inf])
# self.observation_space = spaces.Box(low=low, high=high)
#else:
# high = np.array([1., np.inf, np.inf, np.inf, 1., np.inf, 1., np.inf, 255])
# low = np.array([0., -np.inf, -np.inf, -np.inf, 0., -np.inf
def step(self, u):
client = self.client
this_action = self.agent_to_torcs(u)
# Apply Action
action_torcs = client.R.d
# Steering
client.R.d['steer'] = this_action['steer'] # in [-1, 1]
# Simple Autnmatic Throttle Control by Snakeoil
if self.throttle is False:
target_speed = self.default_speed
if client.S.d['speedX'] < target_speed - (client.R.d['steer']*50):
client.R.d['accel'] += .01
else:
client.R.d['accel'] -= .01
if client.R.d['accel'] > 0.2:
client.R.d['accel'] = 0.2
if client.S.d['speedX'] < 10:
client.R.d['accel'] += 1/(client.S.d['speedX']+.1)
# Traction Control System
if ((client.S.d['wheelSpinVel'][2]+client.S.d['wheelSpinVel'][3]) -
(client.S.d['wheelSpinVel'][0]+client.S.d['wheelSpinVel'][1]) > 5):
client.R.d['accel'] -= .2
else:
client.R.d['accel'] = this_action['accel']
client.R.d['brake'] = this_action['brake']
# Automatic Gear Change by Snakeoil
if self.gear_change is True:
client.R.d['gear'] = this_action['gear']
else:
# Automatic Gear Change by Snakeoil is possible
client.R.d['gear'] = 1
if self.throttle:
if client.S.d['speedX'] > 50:
client.R.d['gear'] = 2
if client.S.d['speedX'] > 80:
client.R.d['gear'] = 3
if client.S.d['speedX'] > 110:
client.R.d['gear'] = 4
if client.S.d['speedX'] > 140:
client.R.d['gear'] = 5
if client.S.d['speedX'] > 170:
client.R.d['gear'] = 6
# Save the privious full-obs from torcs for the reward calculation
obs_pre = copy.deepcopy(client.S.d)
# One-Step Dynamics Update #################################
# Apply the Agent's action into torcs, zhichen: send R.d
client.respond_to_server()
# Get the response of TORCS, zhichen: set S.d
client.get_servers_input()
# Get the current full-observation from torcs
obs = client.S.d
# Make an obsevation from a raw observation vector from TORCS
self.observation = self.make_observaton(obs)
observation_next = self.observation
# Reward setting Here #######################################
# direction-dependent positive reward
track = np.array(obs['track'])
trackPos = np.array(obs['trackPos'])
sp = np.array(obs['speedX'])
damage = np.array(obs['damage'])
rpm = np.array(obs['rpm'])
progress = sp*np.cos(obs['angle']) - np.abs(sp*np.sin(obs['angle'])) - sp * np.abs(obs['trackPos'])
reward = progress
# collision detection
if obs['damage'] - obs_pre['damage'] > 0:
reward = -1
#
# Termination judgement #########################
#
episode_terminate = False
if self.terminal_judge_start < self.time_step:
if abs(track.any()) > 1 or abs(trackPos) > 2.7: # Episode is terminated if the car is out of track
print('Out of Track', track.any(), trackPos)
reward = -200
episode_terminate = True
client.R.d['meta'] = True
if self.terminal_judge_start < self.time_step: # Episode terminates if the progress of agent is small
if sp < self.termination_limit_progress:
print('No progress', progress, sp)
reward = -100
episode_terminate = True
client.R.d['meta'] = True
#if np.cos(obs['angle']) < -0.5: # Episode is terminated if the agent runs backward
# print('Running Backward')
# reward = -1
# episode_terminate = True
# client.R.d['meta'] = True
if np.cos(obs['angle']) < 0: # Episode is terminated if the agent runs backward
print('Running Backward')
reward = -100
episode_terminate = True
client.R.d['meta'] = True
info = {}
info['distRaced'] = observation_next.distRaced
info['distFromStart'] = observation_next.distFromStart
info['lastLapTime'] = observation_next.lastLapTime
info['curLapTime'] = observation_next.curLapTime
info['speed'] = sp
if client.R.d['meta'] is True: # Send a reset signal
self.initial_run = False
client.respond_to_server() # zhichen: send meta signal
self.time_step += 1
return observation_next, reward, client.R.d['meta'], info #self.get_obs()
# zhichen: relaunch torcs env and rebuild client-server connection
def reset(self, relaunch=False):
self.time_step = 0
if self.initial_reset is not True:
self.client.R.d['meta'] = True
self.client.respond_to_server()
## TENTATIVE. Restarting TORCS every episode suffers the memory leak bug!
if relaunch is True:
self.reset_torcs()
logging.info("### TORCS is RELAUNCHED ###")
# Modify here if you use multiple tracks in the environment
# self.client.shutdown()
self.client = snakeoil3.Client(p=3101, vision=self.vision, track_name=self.track_name) # # Open new UDP in vtorcs
self.client.MAX_STEPS = np.inf
client = self.client
client.get_servers_input() # Get the initial input from torcs
logging.info('Successfully reset S')
obs = client.S.d # Get the current full-observation from torcs
self.observation = self.make_observaton(obs)
self.last_u = None
self.initial_reset = False
return self.get_obs()
def end(self):
os.system('pkill torcs')
def get_obs(self):
return self.observation
def reset_torcs(self):
#print("relaunch torcs")
os.system('pkill torcs')
time.sleep(0.5)
if self.vision is True:
config_string = 'torcs -s -r /usr/local/share/games/torcs/config/raceman/' + self.track_name + ' -nofuel -nodamage -nolaptime -vision &'
os.system(config_string)
else:
os.system('torcs -s -r /usr/local/share/games/torcs/config/raceman/' + self.track_name + ' -T -nofuel -nodamage -nolaptime &')
time.sleep(0.5)
os.system('sh autostart.sh')
time.sleep(0.5)
#config_string = 'torcs -s -r /usr/local/share/games/torcs/config/raceman/' + self.track_name + ' -T -nofuel &'
#os.system(config_string)
def agent_to_torcs(self, u):
torcs_action = {'steer': u[0]}
if self.throttle is True: # throttle action is enabled
torcs_action.update({'accel': u[1]})
torcs_action.update({'brake': u[2]})
if self.gear_change is True: # gear change action is enabled
torcs_action.update({'gear': int(u[3])})
return torcs_action
def obs_vision_to_image_rgb(self, obs_image_vec):
image_vec = obs_image_vec
r = image_vec[0:len(image_vec):3]
g = image_vec[1:len(image_vec):3]
b = image_vec[2:len(image_vec):3]
sz = (64, 64)
r = np.array(r).reshape(sz)
g = np.array(g).reshape(sz)
b = np.array(b).reshape(sz)
return np.array([r, g, b], dtype=np.uint8)
def make_observaton(self, raw_obs):
if self.vision is False:
names = ['speedX', 'speedY', 'speedZ', 'angle', 'rpm',
'track',
'trackPos',
'wheelSpinVel', 'lastLapTime', 'curLapTime','distRaced', 'distFromStart']
#'distFromStartLine']
Observation = col.namedtuple('Observaion', names)
return Observation(speedX=np.array(raw_obs['speedX'], dtype=np.float32)/300.0,
speedY=np.array(raw_obs['speedY'], dtype=np.float32)/300.0,
speedZ=np.array(raw_obs['speedZ'], dtype=np.float32)/300.0,
angle=np.array(raw_obs['angle'], dtype=np.float32)/3.1416,
rpm=np.array(raw_obs['rpm'], dtype=np.float32)/10000,
track=np.array(raw_obs['track'], dtype=np.float32)/200.,
trackPos=np.array(raw_obs['trackPos'], dtype=np.float32)/1.,
wheelSpinVel=np.array(raw_obs['wheelSpinVel'], dtype=np.float32),
lastLapTime=np.array(raw_obs['lastLapTime'], dtype=np.float32)/1.0,
curLapTime=np.array(raw_obs['curLapTime'], dtype=np.float32)/1.0,
distRaced=np.array(raw_obs['distRaced'], dtype=np.float32)/1.0,
distFromStart=np.array(raw_obs['distFromStart'], dtype=np.float32)/1.0)
else:
names = ['focus',
'speedX', 'speedY', 'speedZ', 'angle',
'opponents',
'rpm',
'track',
'trackPos',
'wheelSpinVel',
'img']
Observation = col.namedtuple('Observaion', names)
# Get RGB from observation
image_rgb = self.obs_vision_to_image_rgb(raw_obs[names[8]])
return Observation(focus=np.array(raw_obs['focus'], dtype=np.float32)/200.,
speedX=np.array(raw_obs['speedX'], dtype=np.float32)/self.default_speed,
speedY=np.array(raw_obs['speedY'], dtype=np.float32)/self.default_speed,
speedZ=np.array(raw_obs['speedZ'], dtype=np.float32)/self.default_speed,
opponents=np.array(raw_obs['opponents'], dtype=np.float32)/200.,
rpm=np.array(raw_obs['rpm'], dtype=np.float32),
track=np.array(raw_obs['track'], dtype=np.float32)/200.,
trackPos=np.array(raw_obs['trackPos'], dtype=np.float32)/1.,
wheelSpinVel=np.array(raw_obs['wheelSpinVel'], dtype=np.float32),
img=image_rgb)