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neural_update.py
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neural_update.py
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from gym_torcs import TorcsEnv
import numpy as np
import random
import argparse
from keras.models import model_from_json, Model
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.optimizers import Adam
import tensorflow as tf
import json
import logging
import sys
import datetime
import time
from replay_buffer import ReplayBuffer
from actor_network import ActorNetwork
from critic_network import CriticNetwork
import functools
import copy
from utils import *
import os
import pickle
def clip(v,lo,hi):
if v<lo: return lo
elif v>hi: return hi
else: return v
class FunctionOU(object):
def function(self, x, mu, theta, sigma):
return theta * (mu - x) + sigma * np.random.randn(1)
class NeuralAgent():
def __init__(self, track_name='practgt2.xml'):
BUFFER_SIZE = 100000
TAU = 0.001 # Target Network HyperParameters
LRA = 0.0001 # Learning rate for Actor
LRC = 0.001 # Lerning rate for Critic
state_dim = 29 # of sensors input
self.batch_size = 32
self.lambda_mix = 10.0
self.action_dim = 3 # Steering/Acceleration/Brake
# Tensorflow GPU optimization
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
from keras import backend as K
K.set_session(sess)
self.actor = ActorNetwork(sess, state_dim, self.action_dim, self.batch_size, TAU, LRA)
self.critic = CriticNetwork(sess, state_dim, self.action_dim, self.batch_size, TAU, LRC)
self.buff = ReplayBuffer(BUFFER_SIZE) # Create replay buffer
self.track_name = track_name
self.save = dict(total_reward=[],
total_step=[],
ave_reward=[],
distRaced=[],
distFromStart=[],
lastLapTime=[],
curLapTime=[],
lapTimes=[],
avelapTime=[],
ave_sp=[],
max_sp=[],
min_sp=[],
test_total_reward=[],
test_total_step=[],
test_ave_reward=[],
test_distRaced=[],
test_distFromStart=[],
test_lastLapTime=[],
test_curLapTime=[],
test_lapTimes = [],
test_avelapTime=[],
test_ave_sp=[],
test_max_sp=[],
test_min_sp=[]
)
def rollout(self, env):
max_steps = 10000
vision = False
# zhichen: it is not stable to have two torcs env and UDP connections
# env = TorcsEnv(vision=vision, throttle=True, gear_change=False, track_name=self.track_name)
ob = env.reset(relaunch=True)
s_t = np.hstack(
(ob.speedX, ob.angle, ob.trackPos, ob.speedY, ob.speedZ, ob.rpm, ob.wheelSpinVel / 100.0, ob.track))
total_reward = 0.
sp = []
lastLapTime = []
for j_iter in range(max_steps):
a_t = self.actor.model.predict(s_t.reshape(1, s_t.shape[0]))
a_t = a_t[0]
# print('test a_t:', a_t)
a_t[0]= clip(a_t[0], -1, 1)
a_t[1]= clip(a_t[1], 0, 1)
a_t[2]= clip(a_t[2], 0, 1)
ob, r_t, done, info = env.step(a_t)
sp.append(info['speed'])
if lastLapTime == []:
if info['lastLapTime']>0:
lastLapTime.append(info['lastLapTime'])
elif info['lastLapTime']>0 and lastLapTime[-1] != info['lastLapTime']:
lastLapTime.append(info['lastLapTime'])
if np.mod(j_iter +1,20) == 0:
logging.info('step: ' + str(j_iter+1))
print('\n ob: ', ob)
s_t = np.hstack(
(ob.speedX, ob.angle, ob.trackPos, ob.speedY, ob.speedZ, ob.rpm, ob.wheelSpinVel / 100.0, ob.track))
total_reward += r_t
if done: break
logging.info("Test Episode Reward: " + str(total_reward) +
" Episode Length: " + str(j_iter+1) + " Ave Reward: " + str(total_reward/(j_iter+1)) +
"\n Distance: " + str(info['distRaced']) + ' ' + str(info['distFromStart']) +
"\n Last Lap Times: " + str(info['lastLapTime']) + " Cur Lap Times: " + str(info['curLapTime']) + " lastLaptime: " + str(lastLapTime) +
"\n ave sp: " + str(np.mean(sp)) + " max sp: " + str(np.max(sp)) )
#logging.info(" Total Steps: " + str(step) + " " + str(i_episode) + "-th Episode Reward: " + str(total_reward) +
# " Episode Length: " + str(j_iter+1) + " Distance" + str(ob.distRaced) + " Lap Times: " + str(ob.lastLapTime))
#env.end() # This is for shutting down TORCS
ave_sp = np.mean(sp)
max_sp = np.max(sp)
min_sp = np.min(sp)
return total_reward, j_iter+1, info, ave_sp, max_sp, min_sp, lastLapTime
def update_neural(self, controllers, episode_count=200, tree=False, seed=1337):
OU = FunctionOU()
vision = False
GAMMA = 0.99
EXPLORE = 100000.
max_steps = 10000
reward = 0
done = False
step = 0
epsilon = 1
if not tree:
steer_prog, accel_prog, brake_prog = controllers
# Generate a Torcs environment
env = TorcsEnv(vision=vision, throttle=True, gear_change=False, track_name=self.track_name)
window = 5
lambda_store = np.zeros((max_steps, 1))
lambda_max = 40.
factor = 0.8
logging.info("TORCS Experiment Start with Lambda = " + str(self.lambda_mix))
for i_episode in range(episode_count):
logging.info("Episode : " + str(i_episode) + " Replay Buffer " + str(self.buff.count()))
if np.mod(i_episode, 3) == 0:
logging.info('relaunch TORCS')
ob = env.reset(relaunch=True) # relaunch TORCS every 3 episode because of the memory leak error
else:
logging.info('reset TORCS')
ob = env.reset()
#[ob.speedX, ob.angle, ob.trackPos, ob.speedY, ob.speedZ, ob.rpm, list(ob.wheelSpinVel / 100.0), list(ob.track)]
s_t = np.hstack(
(ob.speedX, ob.angle, ob.trackPos, ob.speedY, ob.speedZ, ob.rpm, ob.wheelSpinVel / 100.0, ob.track))
total_reward = 0.
tempObs = [[ob.speedX], [ob.angle], [ob.trackPos], [ob.speedY], [ob.speedZ], [ob.rpm],
list(ob.wheelSpinVel / 100.0), list(ob.track), [0, 0, 0]]
window_list = [tempObs[:] for _ in range(window)]
sp = []
lastLapTime = []
for j_iter in range(max_steps):
if tree:
tree_obs = [sensor for obs in tempObs[:-1] for sensor in obs]
act_tree = controllers.predict([tree_obs])
steer_action = clip_to_range(act_tree[0][0], -1, 1)
accel_action = clip_to_range(act_tree[0][1], 0, 1)
brake_action = clip_to_range(act_tree[0][2], 0, 1)
else:
steer_action = clip_to_range(steer_prog.pid_execute(window_list), -1, 1)
accel_action = clip_to_range(accel_prog.pid_execute(window_list), 0, 1)
brake_action = clip_to_range(brake_prog.pid_execute(window_list), 0, 1)
action_prior = [steer_action, accel_action, brake_action]
tempObs = [[ob.speedX], [ob.angle], [ob.trackPos], [ob.speedY], [ob.speedZ], [ob.rpm],
list(ob.wheelSpinVel / 100.0), list(ob.track), action_prior]
window_list.pop(0)
window_list.append(tempObs[:])
loss = 0
epsilon -= 1.0 / EXPLORE
a_t = np.zeros([1, self.action_dim])
noise_t = np.zeros([1, self.action_dim])
a_t_original = self.actor.model.predict(s_t.reshape(1, s_t.shape[0]))
noise_t[0][0] = max(epsilon, 0) * OU.function(a_t_original[0][0], 0.0, 0.60, 0.30)
noise_t[0][1] = max(epsilon, 0) * OU.function(a_t_original[0][1], 0.5, 1.00, 0.10)
noise_t[0][2] = max(epsilon, 0) * OU.function(a_t_original[0][2], 0, 1.00, 0.05)
a_t[0][0] = a_t_original[0][0] + noise_t[0][0]
a_t[0][1] = a_t_original[0][1] + noise_t[0][1]
a_t[0][2] = a_t_original[0][2] + noise_t[0][2]
mixed_act = [a_t[0][k_iter] / (1 + self.lambda_mix) + (self.lambda_mix / (1 + self.lambda_mix)) * action_prior[k_iter] for k_iter in range(3)]
ob, r_t, done, info = env.step(mixed_act)
sp.append(info['speed'])
if lastLapTime == []:
if info['lastLapTime']>0:
lastLapTime.append(info['lastLapTime'])
elif info['lastLapTime']>0 and lastLapTime[-1] != info['lastLapTime']:
lastLapTime.append(info['lastLapTime'])
s_t1 = np.hstack(
(ob.speedX, ob.angle, ob.trackPos, ob.speedY, ob.speedZ, ob.rpm, ob.wheelSpinVel / 100.0, ob.track))
self.buff.add(s_t, a_t[0], r_t, s_t1, done) # Add replay buffer
# Do the batch update
batch = self.buff.getBatch(self.batch_size)
states = np.asarray([e[0] for e in batch])
actions = np.asarray([e[1] for e in batch])
rewards = np.asarray([e[2] for e in batch])
new_states = np.asarray([e[3] for e in batch])
dones = np.asarray([e[4] for e in batch])
y_t = np.zeros((states.shape[0],1))
target_q_values = self.critic.target_model.predict([new_states, self.actor.target_model.predict(new_states)])
for k in range(len(batch)):
if dones[k]:
y_t[k] = rewards[k]
else:
y_t[k] = rewards[k] + GAMMA * target_q_values[k]
loss += self.critic.model.train_on_batch([states, actions], y_t)
a_for_grad = self.actor.model.predict(states)
grads = self.critic.gradients(states, a_for_grad)
self.actor.train(states, grads)
self.actor.target_train()
self.critic.target_train()
total_reward += r_t
s_t = s_t1
# Control prior mixing term
if j_iter > 0 and i_episode > 50:
lambda_track = lambda_max * (1 - np.exp(-factor * np.abs(r_t + GAMMA * np.mean(target_q_values[-1] - base_q[-1]))))
lambda_track = np.squeeze(lambda_track)
else:
lambda_track = 10.
lambda_store[j_iter] = lambda_track
base_q = copy.deepcopy(target_q_values)
if np.mod(step, 2000) == 0:
logging.info("Episode " + str(i_episode) + " Distance " + str(ob.distRaced) + " Lap Times " + str(ob.lastLapTime))
step += 1
if done:
break
#else:
# env.end()
self.lambda_mix = np.mean(lambda_store)
logging.info('Episode ends! \n' +
"Total Steps: " + str(step) + " " + str(i_episode) + "-th Episode Reward: " + str(total_reward) +
" Episode Length: " + str(j_iter+1) + " Ave Reward: " + str(total_reward/(j_iter+1)) +
"\n Distance: " + str(info['distRaced']) + ' ' + str(info['distFromStart']) +
"\n Last Lap Times: " + str(info['lastLapTime']) + " Cur Lap Times: " + str(info['curLapTime']) + " lastLaptime: " + str(lastLapTime) +
"\n ave sp: " + str(np.mean(sp)) + " max sp: " + str(np.max(sp)) )
#logging.info(" Lambda Mix: " + str(self.lambda_mix))
self.save['total_reward'].append(total_reward)
self.save['total_step'].append(j_iter+1)
self.save['ave_reward'].append(total_reward/(j_iter+1))
self.save['distRaced'].append(info['distRaced'])
self.save['distFromStart'].append(info['distFromStart'])
self.save['lastLapTime'].append(info['lastLapTime'])
self.save['curLapTime'].append(info['curLapTime'])
self.save['lapTimes'].append(lastLapTime)
if lastLapTime == []:
self.save['avelapTime'].append(0)
else:
self.save['avelapTime'].append(np.mean(lastLapTime))
self.save['ave_sp'].append(np.mean(sp))
self.save['max_sp'].append(np.max(sp))
self.save['min_sp'].append(np.min(sp))
# test
if np.mod(i_episode+1, 10) == 0:
logging.info("Start Testing!")
test_total_reward, test_step, test_info, test_ave_sp, test_max_sp, test_min_sp, test_lastLapTime = self.rollout(env)
self.save['test_total_reward'].append(test_total_reward)
self.save['test_total_step'].append(test_step)
self.save['test_ave_reward'].append(test_total_reward/test_step)
self.save['test_distRaced'].append(test_info['distRaced'])
self.save['test_distFromStart'].append(test_info['distFromStart'])
self.save['test_lastLapTime'].append(test_info['lastLapTime'])
self.save['test_curLapTime'].append(test_info['curLapTime'])
self.save['test_lapTimes'].append(test_lastLapTime)
if test_lastLapTime == []:
self.save['test_avelapTime'].append(0)
else:
self.save['test_avelapTime'].append(np.mean(test_lastLapTime))
self.save['test_ave_sp'].append(test_ave_sp)
self.save['test_max_sp'].append(test_max_sp)
self.save['test_min_sp'].append(test_min_sp)
if np.mod(i_episode+1, 5) == 0:
print("Now we save model")
#os.remove("actormodel.h5")
self.actor.model.save_weights("actormodel_"+str(seed)+".h5", overwrite=True)
with open("actormodel.json", "w") as outfile:
json.dump(self.actor.model.to_json(), outfile)
#os.remove("criticmodel.h5")
self.critic.model.save_weights("criticmodel_"+str(seed)+".h5", overwrite=True)
with open("criticmodel.json", "w") as outfile:
json.dump(self.critic.model.to_json(), outfile)
filename = "./model/actormodel_"+str(seed)+'_'+str(i_episode+1)+".h5"
dirname = os.path.dirname(filename)
if not os.path.exists(dirname):
os.makedirs(dirname)
self.actor.model.save_weights(filename, overwrite=True)
filename = "./model/criticmodel_"+str(seed)+'_'+str(i_episode+1)+".h5"
dirname = os.path.dirname(filename)
if not os.path.exists(dirname):
os.makedirs(dirname)
self.critic.model.save_weights(filename, overwrite=True)
if np.mod(i_episode+1, 10) == 0:
filename = "./Fig/iprl_save_" + str(seed)
dirname = os.path.dirname(filename)
if not os.path.exists(dirname):
os.makedirs(dirname)
with open(filename,'wb') as f:
pickle.dump(self.save, f)
if i_episode>1000 and all(np.array(self.save['total_reward'][-20:])<20):
print('model degenerated. Stop at Epsisode '+ str(i_episode))
break
env.end() # This is for shutting down TORCS
logging.info("Neural Policy Update Finish.")
return None
def collect_data(self, controllers, tree=False):
vision = False
max_steps = 10000
step = 0
if not tree:
steer_prog, accel_prog, brake_prog = controllers
# Generate a Torcs environment
env = TorcsEnv(vision=vision, throttle=True, gear_change=False, track_name=self.track_name)
ob = env.reset(relaunch=True)
print("S0=", ob)
window = 5
lambda_store = np.zeros((max_steps, 1))
lambda_max = 40.
factor = 0.8
logging.info("TORCS Collection started with Lambda = " + str(self.lambda_mix))
s_t = np.hstack(
(ob.speedX, ob.angle, ob.trackPos, ob.speedY, ob.speedZ, ob.rpm, ob.wheelSpinVel / 100.0, ob.track))
total_reward = 0.
tempObs = [[ob.speedX], [ob.angle], [ob.trackPos], [ob.speedY], [ob.speedZ], [ob.rpm],
list(ob.wheelSpinVel / 100.0), list(ob.track), [0, 0, 0]]
window_list = [tempObs[:] for _ in range(window)]
observation_list = []
actions_list = []
lastLapTime = []
sp =[]
for j_iter in range(max_steps):
if tree:
tree_obs = [sensor for obs in tempObs[:-1] for sensor in obs]
act_tree = controllers.predict([tree_obs])
steer_action = clip_to_range(act_tree[0][0], -1, 1)
accel_action = clip_to_range(act_tree[0][1], 0, 1)
brake_action = clip_to_range(act_tree[0][2], 0, 1)
else:
steer_action = clip_to_range(steer_prog.pid_execute(window_list), -1, 1)
accel_action = clip_to_range(accel_prog.pid_execute(window_list), 0, 1)
brake_action = clip_to_range(brake_prog.pid_execute(window_list), 0, 1)
action_prior = [steer_action, accel_action, brake_action]
tempObs = [[ob.speedX], [ob.angle], [ob.trackPos], [ob.speedY], [ob.speedZ], [ob.rpm],
list(ob.wheelSpinVel / 100.0), list(ob.track), action_prior]
window_list.pop(0)
window_list.append(tempObs[:])
a_t = self.actor.model.predict(s_t.reshape(1, s_t.shape[0]))
mixed_act = [a_t[0][k_iter] / (1 + self.lambda_mix) + (self.lambda_mix / (1 + self.lambda_mix)) * action_prior[k_iter] for k_iter in range(3)]
if tree:
newobs = [item for sublist in tempObs[:-1] for item in sublist]
observation_list.append(newobs[:])
else:
observation_list.append(window_list[:])
actions_list.append(mixed_act[:])
ob, r_t, done, info = env.step(mixed_act)
sp.append(info['speed'])
if lastLapTime == []:
if info['lastLapTime']>0:
lastLapTime.append(info['lastLapTime'])
elif info['lastLapTime']>0 and lastLapTime[-1] != info['lastLapTime']:
lastLapTime.append(info['lastLapTime'])
s_t1 = np.hstack(
(ob.speedX, ob.angle, ob.trackPos, ob.speedY, ob.speedZ, ob.rpm, ob.wheelSpinVel / 100.0, ob.track))
total_reward += r_t
s_t = s_t1
#if np.mod(step, 2000) == 0:
# logging.info(" Distance " + str(ob.distRaced) + " Lap Times " + str(ob.lastLapTime))
step += 1
if done:
break
logging.info("Data Collection Finished!")
logging.info('Episode ends! \n' +
"Episode Reward: " + str(total_reward) +
" Episode Length: " + str(j_iter+1) + " Ave Reward: " + str(total_reward/(j_iter+1)) +
"\n Distance: " + str(info['distRaced']) + ' ' + str(info['distFromStart']) +
"\n Last Lap Times: " + str(info['lastLapTime']) + " Cur Lap Times: " + str(info['curLapTime']) + " lastLaptime: " + str(lastLapTime) +
"\n ave sp: " + str(np.mean(sp)) + " max sp: " + str(np.max(sp)) )
env.end()
return observation_list, actions_list
def label_data(self, controllers, observation_list, tree=False):
if not tree:
steer_prog, accel_prog, brake_prog = controllers
actions_list = []
net_obs_list = []
logging.info("Data labelling started with Lambda = " + str(self.lambda_mix))
for window_list in observation_list:
if tree:
act_tree = controllers.predict([window_list])
steer_action = clip_to_range(act_tree[0][0], -1, 1)
accel_action = clip_to_range(act_tree[0][1], 0, 1)
brake_action = clip_to_range(act_tree[0][2], 0, 1)
net_obs_list.append(window_list)
else:
steer_action = clip_to_range(steer_prog.pid_execute(window_list), -1, 1)
accel_action = clip_to_range(accel_prog.pid_execute(window_list), 0, 1)
brake_action = clip_to_range(brake_prog.pid_execute(window_list), 0, 1)
net_obs = [sensor for obs in window_list[-1] for sensor in obs]
net_obs_list.append(net_obs[:29])
action_prior = [steer_action, accel_action, brake_action]
s_t = np.hstack([[net_obs[:29]]])
a_t = self.actor.model.predict(s_t.reshape(1, 29))
mixed_act = [a_t[0][k_iter] / (1 + self.lambda_mix) + (self.lambda_mix / (1 + self.lambda_mix)) * action_prior[k_iter] for k_iter in range(3)]
actions_list.append(mixed_act[:])
return net_obs_list, observation_list, actions_list