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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 snakeoil3_gym as snakeoil3
import numpy as np
import numpy as np
import copy
import collections as col
import os
import time
import random
class TorcsEnv:
terminal_judge_start = 100 # If after 100 timestep still no progress, terminated
termination_limit_progress = 1 # [km/h], episode terminates if car is running slower than this limit
default_speed = 50
initial_reset = False
obs_dim = 29
act_dim = 3
def __init__(self, vision=False, throttle=False, gear_change=False):
self.vision = vision
self.throttle = throttle
self.gear_change = gear_change
self.initial_run = True
self.time_step = 0
self.currState = None
# Now the action_space and observation_space are actually being used, just like in OpenAI's gym
if throttle is False: # Throttle is generally True
self.action_space = spaces.Box(low=-1.0, high=1.0, shape=(1,))
else:
high = np.array([1., 1., 1.])
low = np.array([-1., 0., 0.])
self.action_space = spaces.Box(low=low, high=high) # steer, accel, brake (according to agent_to_torcs() (check the function definition))
if vision is False: # Vision is True when you need the images from the simulator
high = np.inf*np.ones(self.obs_dim)
low = -high
self.observation_space = spaces.Box(low, high)
# just like in https://github.com/openai/gym/blob/master/gym/envs/mujoco/mujoco_env.py#L50 (as of 30/5/17)
else:
high = np.array([1., np.inf, np.inf, np.inf, 1., np.inf, 1., np.inf, 255], dtype=theano.config.floatX)
low = np.array([0., -np.inf, -np.inf, -np.inf, 0., -np.inf, 0., -np.inf, 0], dtype=theano.config.floatX)
self.observation_space = spaces.Box(low=low, high=high)
def terminate(self):
episode_terminate = True
client.R.d['meta'] = True
print('Terminating because bad episode')
def step(self, step, client, u, early_stop):
# 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 Automatic 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']
action_torcs['brake'] = this_action['brake']
# 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 self.throttle:
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 previous 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
code = client.get_servers_input(step)
if code==-1:
client.R.d['meta'] = True
print('Terminating because server stopped responding')
return None, 0, client.R.d['meta'], {'termination_cause':'hardReset'}
# 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_observation(obs)
self.currState = np.hstack((self.observation.angle, self.observation.track, self.observation.trackPos,
self.observation.speedX, self.observation.speedY, self.observation.speedZ,
self.observation.wheelSpinVel/100.0, self.observation.rpm))
# 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 ( (abs(track.any()) > 1 or abs(trackPos) > 1) and early_stop ): # Episode is terminated if the car is out of track
reward = -200
episode_terminate = True
client.R.d['meta'] = True
print('Terminating because Out of Track')
# if self.terminal_judge_start < self.time_step: # Episode terminates if the progress of agent is small
# if ( (progress < self.termination_limit_progress) and early_stop ):
# print("No progress")
# #episode_terminate = True
# client.R.d['meta'] = True
# #print('Terminating because Small Progress')
if np.cos(obs['angle']) < 0: # Episode is terminated if the agent runs backward
episode_terminate = True
client.R.d['meta'] = True
print('Terminating because Turned Back')
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.observation, reward, client.R.d['meta'], {}
# return reward
def reset(self, client, relaunch=False):
#print("Reset")
port = client.port
self.time_step = 0
# print '111'
if self.initial_reset is not True:
client.R.d['meta'] = True
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
client = snakeoil3.Client(p=port, vision=self.vision) # Open new UDP in vtorcs
client.MAX_STEPS = np.inf
# client = self.client
client.get_servers_input(-1) # Get the initial input from torcs
obs = client.S.d # Get the current full-observation from torcs
self.observation = self.make_observation(obs)
self.currState = np.hstack((self.observation.angle, self.observation.track, self.observation.trackPos,
self.observation.speedX, self.observation.speedY, self.observation.speedZ,
self.observation.wheelSpinVel/100.0, self.observation.rpm))
self.last_u = None
self.initial_reset = False
return self.get_obs(), client
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 -nolaptime &')
time.sleep(0.5)
os.system('sh scripts/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 # This is generally true
torcs_action.update({'accel': u[1]})
torcs_action.update({'brake': u[2]})
if self.gear_change is True: # gear change action is enabled # This is generally false
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_observation(self, raw_obs):
if self.vision is False:
names = ['focus',
'speedX', 'speedY', 'speedZ', 'angle', 'damage',
'opponents',
'rpm',
'track',
'trackPos',
'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)/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,
damage=np.array(raw_obs['damage'], dtype=np.float32),
opponents=np.array(raw_obs['opponents'], dtype=np.float32)/200.,
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))
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)