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ddpg.py
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ddpg.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
from keras.engine.training import collect_trainable_weights
import json
from ReplayBuffer import ReplayBuffer
from ActorNetwork import ActorNetwork
from CriticNetwork import CriticNetwork
from OU import OU
import timeit
OU = OU() #Ornstein-Uhlenbeck Process
def playGame(train_indicator=0): #1 means Train, 0 means simply Run
BUFFER_SIZE = 100000
BATCH_SIZE = 32
GAMMA = 0.99
TAU = 0.001 #Target Network HyperParameters
LRA = 0.0001 #Learning rate for Actor
LRC = 0.001 #Lerning rate for Critic
action_dim = 3 #Steering/Acceleration/Brake
state_dim = 29 #of sensors input
np.random.seed(1337)
vision = False
EXPLORE = 100000.
episode_count = 2000
max_steps = 100000
reward = 0
done = False
step = 0
epsilon = 1
indicator = 0
#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)
actor = ActorNetwork(sess, state_dim, action_dim, BATCH_SIZE, TAU, LRA)
critic = CriticNetwork(sess, state_dim, action_dim, BATCH_SIZE, TAU, LRC)
buff = ReplayBuffer(BUFFER_SIZE) #Create replay buffer
# Generate a Torcs environment
env = TorcsEnv(vision=vision, throttle=True,gear_change=False)
#Now load the weight
print("Now we load the weight")
try:
actor.model.load_weights("actormodel.h5")
critic.model.load_weights("criticmodel.h5")
actor.target_model.load_weights("actormodel.h5")
critic.target_model.load_weights("criticmodel.h5")
print("Weight load successfully")
except:
print("Cannot find the weight")
print("TORCS Experiment Start.")
for i in range(episode_count):
print("Episode : " + str(i) + " Replay Buffer " + str(buff.count()))
if np.mod(i, 3) == 0:
ob = env.reset(relaunch=True) #relaunch TORCS every 3 episode because of the memory leak error
else:
ob = env.reset()
s_t = np.hstack((ob.angle, ob.track, ob.trackPos, ob.speedX, ob.speedY, ob.speedZ, ob.wheelSpinVel/100.0, ob.rpm))
total_reward = 0.
for j in range(max_steps):
loss = 0
epsilon -= 1.0 / EXPLORE
a_t = np.zeros([1,action_dim])
noise_t = np.zeros([1,action_dim])
a_t_original = actor.model.predict(s_t.reshape(1, s_t.shape[0]))
noise_t[0][0] = train_indicator * max(epsilon, 0) * OU.function(a_t_original[0][0], 0.0 , 0.60, 0.30)
noise_t[0][1] = train_indicator * max(epsilon, 0) * OU.function(a_t_original[0][1], 0.5 , 1.00, 0.10)
noise_t[0][2] = train_indicator * max(epsilon, 0) * OU.function(a_t_original[0][2], -0.1 , 1.00, 0.05)
#The following code do the stochastic brake
#if random.random() <= 0.1:
# print("********Now we apply the brake***********")
# noise_t[0][2] = train_indicator * max(epsilon, 0) * OU.function(a_t_original[0][2], 0.2 , 1.00, 0.10)
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]
ob, r_t, done, info = env.step(a_t[0])
s_t1 = np.hstack((ob.angle, ob.track, ob.trackPos, ob.speedX, ob.speedY, ob.speedZ, ob.wheelSpinVel/100.0, ob.rpm))
buff.add(s_t, a_t[0], r_t, s_t1, done) #Add replay buffer
#Do the batch update
batch = buff.getBatch(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.asarray([e[1] for e in batch])
target_q_values = critic.target_model.predict([new_states, 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]
if (train_indicator):
loss += critic.model.train_on_batch([states,actions], y_t)
a_for_grad = actor.model.predict(states)
grads = critic.gradients(states, a_for_grad)
actor.train(states, grads)
actor.target_train()
critic.target_train()
total_reward += r_t
s_t = s_t1
print("Episode", i, "Step", step, "Action", a_t, "Reward", r_t, "Loss", loss)
step += 1
if done:
break
if np.mod(i, 3) == 0:
if (train_indicator):
print("Now we save model")
actor.model.save_weights("actormodel.h5", overwrite=True)
with open("actormodel.json", "w") as outfile:
json.dump(actor.model.to_json(), outfile)
critic.model.save_weights("criticmodel.h5", overwrite=True)
with open("criticmodel.json", "w") as outfile:
json.dump(critic.model.to_json(), outfile)
print("TOTAL REWARD @ " + str(i) +"-th Episode : Reward " + str(total_reward))
print("Total Step: " + str(step))
print("")
env.end() # This is for shutting down TORCS
print("Finish.")
if __name__ == "__main__":
playGame()