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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
# from os import path
import snakeoil3_gym as snakeoil3
import numpy as np
import copy
import collections as col
import os
import time
class TorcsEnv:
terminal_judge_start = 500 # Speed limit is applied after this step
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):
#print("Init")
self.vision = vision
self.throttle = throttle
self.gear_change = gear_change
self.initial_run = True
##print("launch torcs")
os.system('pkill torcs')
time.sleep(0.5)
if self.vision is True:
os.system('torcs -nofuel -nodamage -nolaptime -vision &')
else:
os.system('torcs -nofuel -nodamage -nolaptime &')
time.sleep(0.5)
os.system('sh autostart.sh')
time.sleep(0.5)
"""
# 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(low=-1.0, high=1.0, shape=(2,))
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, 0., -np.inf, 0])
self.observation_space = spaces.Box(low=low, high=high)
def step(self, u):
#print("Step")
# convert thisAction to the actual torcs actionstr
client = self.client
this_action = self.agent_to_torcs(u)
# Apply Action
action_torcs = client.R.d
# Steering
action_torcs['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):
action_torcs['accel'] -= .2
else:
action_torcs['accel'] = this_action['accel']
# Automatic Gear Change by Snakeoil
if self.gear_change is True:
action_torcs['gear'] = this_action['gear']
else:
# Automatic Gear Change by Snakeoil is possible
action_torcs['gear'] = 1
"""
if client.S.d['speedX'] > 50:
action_torcs['gear'] = 2
if client.S.d['speedX'] > 80:
action_torcs['gear'] = 3
if client.S.d['speedX'] > 110:
action_torcs['gear'] = 4
if client.S.d['speedX'] > 140:
action_torcs['gear'] = 5
if client.S.d['speedX'] > 170:
action_torcs['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
client.respond_to_server()
# Get the response of TORCS
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)
# Reward setting Here #######################################
# direction-dependent positive reward
track = np.array(obs['track'])
sp = np.array(obs['speedX'])
progress = sp*np.cos(obs['angle'])
reward = progress
# collision detection
if obs['damage'] - obs_pre['damage'] > 0:
reward = -1
# Termination judgement #########################
episode_terminate = False
if track.min() < 0: # Episode is terminated if the car is out of track
reward = - 1
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 progress < self.termination_limit_progress:
episode_terminate = True
client.R.d['meta'] = True
if np.cos(obs['angle']) < 0: # Episode is terminated if the agent runs backward
episode_terminate = True
client.R.d['meta'] = True
if client.R.d['meta'] is True: # Send a reset signal
self.initial_run = False
client.respond_to_server()
self.time_step += 1
return self.get_obs(), reward, client.R.d['meta'], {}
def reset(self, relaunch=False):
#print("Reset")
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()
print("### TORCS is RELAUNCHED ###")
# 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
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:
os.system('torcs -nofuel -nodamage -nolaptime -vision &')
else:
os.system('torcs -nofuel -nodamage -nolaptime &')
time.sleep(0.5)
os.system('sh autostart.sh')
time.sleep(0.5)
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]})
if self.gear_change is True: # gear change action is enabled
torcs_action.update({'gear': u[2]})
return torcs_action
def obs_vision_to_image_rgb(self, obs_image_vec):
image_vec = obs_image_vec
rgb = []
temp = []
# convert size 64x64x3 = 12288 to 64x64=4096 2-D list
# with rgb values grouped together.
# Format similar to the observation in openai gym
for i in range(0,12286,3):
temp.append(image_vec[i])
temp.append(image_vec[i+1])
temp.append(image_vec[i+2])
rgb.append(temp)
temp = []
return np.array(rgb, dtype=np.uint8)
def make_observaton(self, raw_obs):
if self.vision is False:
names = ['focus',
'speedX', 'speedY', 'speedZ',
'opponents',
'rpm',
'track',
'wheelSpinVel']
Observation = col.namedtuple('Observaion', names)
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.,
wheelSpinVel=np.array(raw_obs['wheelSpinVel'], dtype=np.float32))
else:
names = ['focus',
'speedX', 'speedY', 'speedZ',
'opponents',
'rpm',
'track',
'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.,
wheelSpinVel=np.array(raw_obs['wheelSpinVel'], dtype=np.float32),
img=image_rgb)