def train(environment, starting_model_path=None, episodes=15000):
if starting_model_path:
policy_model = DQNModel.load(starting_model_path)
target_model = DQNModel.load(starting_model_path)
print('loaded model {}'.format(starting_model_path))
else:
print('starting model from scratch')
policy_model = DQNModel()
target_model = DQNModel()
target_model.set_weights(policy_model.get_weights())
print('Begin training...')
replay_memory = []
epsilon = 0.0
for episode_i in range(episodes):
replay_memory += play_out_episode(policy_model, environment, epsilon)
replay_memory = replay_memory[-hparams['max_mem_size']:]
epsilon = max(hparams['min_epsilon'], epsilon*hparams['epsilon_decay'])
if len(replay_memory) >= hparams['min_mem_size']:
do_training_step(policy_model, target_model, random.sample(replay_memory, hparams['batch_size']))
if episode_i % hparams['target_model_update_every'] == 0:
target_model.set_weights(policy_model.get_weights())
if episode_i % hparams['evaluation_every'] == 0:
info = evaluate_model(policy_model, environment)
print('===================== episode {}, epsilon {}'.format(episode_i, epsilon))
print(info)
print('======================================')
policy_model.save('checkpoint-{}'.format(episode_i))
def learn_and_evaluate(self):
workers_id = []
batch_size = self.parms['training_episodes'] // self.parms['workers'][0]
for _ in range(self.parms['workers'][0]):
workers_id.append(collecting_worker.remote(self.env, self.model_server, self.memory_server, batch_size))
all_results = []
if self.parms['do_test']:
eval_model = DQNModel(len(env.reset()), len(ACTION_DICT))
learn_done, filedir = False, ""
workers_num = self.parms['workers'][1]
interval = self.parms['test_interval']//workers_num
while not learn_done:
filedir, learn_done = ray.get(self.memory_server.get_evaluate_filedir.remote())
if not filedir:
continue
eval_model.load(filedir)
start_time, total_reward = time.time(), 0
eval_workers = []
for _ in range(workers_num):
eval_workers.append(evaluation_worker_test2.remote(self.env, self.memory_server, eval_model, interval))
avg_reward = sum(ray.get(eval_workers))/workers_num
print(filedir, avg_reward, (time.time() - start_time))
all_results.append(avg_reward)
return all_results
def evaluation_worker(env, mem_server, trials):
eval_model = DQNModel(len(env.reset()), len(ACTION_DICT))
learn_done, filedir = False, ""
while not learn_done:
filedir, learn_done = ray.get(mem_server.get_evaluate_filedir.remote())
if not filedir:
continue
eval_model.load(filedir)
start_time, total_reward = time.time(), 0
for _ in range(trials):
state, done, steps = env.reset(), False, 0
while steps < env._max_episode_steps and not done:
steps += 1
state, reward, done, _ = env.step(eval_model.predict(state))
total_reward += reward
mem_server.add_results.remote(total_reward / trials)
class DQN_server():
def __init__(self, env, hyper_params, action_space):
#self.env = env
#self.max_episode_steps = env._max_episode_steps
"""
beta: The discounted factor of Q-value function
(epsilon): The explore or exploit policy epsilon.
initial_epsilon: When the 'steps' is 0, the epsilon is initial_epsilon, 1
final_epsilon: After the number of 'steps' reach 'epsilon_decay_steps',
The epsilon set to the 'final_epsilon' determinately.
epsilon_decay_steps: The epsilon will decrease linearly along with the steps from 0 to 'epsilon_decay_steps'.
"""
self.beta = hyper_params['beta']
"""
episode: Record training episode
steps: Add 1 when predicting an action
learning: The trigger of agent learning. It is on while training agent. It is off while testing agent.
action_space: The action space of the current environment, e.g 2.
"""
# self.episode = 0
# self.steps = 0
# self.best_reward = 0
# self.learning = True
# self.action_space = action_space
"""
input_len The input length of the neural network. It equals to the length of the state vector.
output_len: The output length of the neural network. It is equal to the action space.
eval_model: The model for predicting action for the agent.
target_model: The model for calculating Q-value of next_state to update 'eval_model'.
use_target_model: Trigger for turn 'target_model' on/off
"""
state = env.reset()
input_len = len(state)
output_len = action_space
self.eval_model = DQNModel(input_len,
output_len,
learning_rate=hyper_params['learning_rate'])
self.use_target_model = hyper_params['use_target_model']
if self.use_target_model:
self.target_model = DQNModel(input_len, output_len)
# memory: Store and sample experience replay.
#self.memory = ReplayBuffer(hyper_params['memory_size'])
"""
batch_size: Mini batch size for training model.
update_steps: The frequence of traning model
model_replace_freq: The frequence of replacing 'target_model' by 'eval_model'
"""
self.batch_size = hyper_params['batch_size']
#self.update_steps = hyper_params['update_steps']
#self.model_replace_freq = hyper_params['model_replace_freq']
print("server initialized")
def replace_target_model(self):
self.target_model.replace(self.eval_model)
def eval_model_predict(self, state):
return self.eval_model.predict(state)
# This next function will be called in the main RL loop to update the neural network model given a batch of experience
# 1) Sample a 'batch_size' batch of experiences from the memory.
# 2) Predict the Q-value from the 'eval_model' based on (states, actions)
# 3) Predict the Q-value from the 'target_model' base on (next_states), and take the max of each Q-value vector, Q_max
# 4) If is_terminal == 1, q_target = reward + discounted factor * Q_max, otherwise, q_target = reward
# 5) Call fit() to do the back-propagation for 'eval_model'.
def update_batch(self, memory):
current_memory_size = memory.get_current_size()
if current_memory_size < self.batch_size:
return
#print("fetching minibatch from replay memory")
batch = memory.sample(self.batch_size)
(states, actions, reward, next_states, is_terminal) = batch
states = states
next_states = next_states
terminal = FloatTensor([1 if t else 0 for t in is_terminal])
reward = FloatTensor(reward)
batch_index = torch.arange(self.batch_size, dtype=torch.long)
# Current Q Values
_, q_values = self.eval_model.predict_batch(states)
#q_values = q_values[np.arange(self.batch_size), actions]
q_values = q_values[batch_index, actions]
# Calculate target
if self.use_target_model:
#print("target_model.predict")
best_actions, q_next = self.target_model.predict_batch(next_states)
else:
best_actions, q_next = self.eval_model.predict_batch(next_states)
q_max = q_next[batch_index, best_actions]
terminal = 1 - terminal
q_max *= terminal
q_target = reward + self.beta * q_max
# update model
self.eval_model.fit(q_values, q_target)
# save model
def save_model(self):
self.eval_model.save(result_floder + '/best_model.pt')
# load model
def load_model(self):
self.eval_model.load(result_floder + '/best_model.pt')
class DQN_agent(object):
def __init__(self, env, hyper_params, action_space=len(ACTION_DICT)):
self.env = env
self.max_episode_steps = env._max_episode_steps
"""
beta: The discounted factor of Q-value function
(epsilon): The explore or exploit policy epsilon.
initial_epsilon: When the 'steps' is 0, the epsilon is initial_epsilon, 1
final_epsilon: After the number of 'steps' reach 'epsilon_decay_steps',
The epsilon set to the 'final_epsilon' determinately.
epsilon_decay_steps: The epsilon will decrease linearly along with the steps from 0 to 'epsilon_decay_steps'.
"""
self.beta = hyper_params['beta']
self.initial_epsilon = 1
self.final_epsilon = hyper_params['final_epsilon']
self.epsilon_decay_steps = hyper_params['epsilon_decay_steps']
"""
episode: Record training episode
steps: Add 1 when predicting an action
learning: The trigger of agent learning. It is on while training agent. It is off while testing agent.
action_space: The action space of the current environment, e.g 2.
"""
self.episode = 0
self.steps = 0
self.best_reward = 0
self.learning = True
self.action_space = action_space
"""
input_len The input length of the neural network. It equals to the length of the state vector.
output_len: The output length of the neural network. It is equal to the action space.
eval_model: The model for predicting action for the agent.
target_model: The model for calculating Q-value of next_state to update 'eval_model'.
use_target_model: Trigger for turn 'target_model' on/off
"""
state = env.reset()
input_len = len(state)
output_len = action_space
self.eval_model = DQNModel(input_len,
output_len,
learning_rate=hyper_params['learning_rate'])
self.use_target_model = hyper_params['use_target_model']
if self.use_target_model:
self.target_model = DQNModel(input_len, output_len)
# memory: Store and sample experience replay.
self.memory = ReplayBuffer(hyper_params['memory_size'])
"""
batch_size: Mini batch size for training model.
update_steps: The frequence of traning model
model_replace_freq: The frequence of replacing 'target_model' by 'eval_model'
"""
self.batch_size = hyper_params['batch_size']
self.update_steps = hyper_params['update_steps']
self.model_replace_freq = hyper_params['model_replace_freq']
print("agent initialized")
# Linear decrease function for epsilon
def linear_decrease(self, initial_value, final_value, curr_steps,
final_decay_steps):
decay_rate = curr_steps / final_decay_steps
if decay_rate > 1:
decay_rate = 1
return initial_value - (initial_value - final_value) * decay_rate
def explore_or_exploit_policy(self, state):
p = uniform(0, 1)
# Get decreased epsilon
epsilon = self.linear_decrease(self.initial_epsilon,
self.final_epsilon, self.steps,
self.epsilon_decay_steps)
#if(np.random.randint(1000)==4):
#print("epsilon",epsilon)
if p < epsilon:
#return action
return randint(0, self.action_space - 1)
else:
#return action
return self.greedy_policy(state)
def greedy_policy(self, state):
return self.eval_model.predict(state)
# This next function will be called in the main RL loop to update the neural network model given a batch of experience
# 1) Sample a 'batch_size' batch of experiences from the memory.
# 2) Predict the Q-value from the 'eval_model' based on (states, actions)
# 3) Predict the Q-value from the 'target_model' base on (next_states), and take the max of each Q-value vector, Q_max
# 4) If is_terminal == 1, q_target = reward + discounted factor * Q_max, otherwise, q_target = reward
# 5) Call fit() to do the back-propagation for 'eval_model'.
def update_batch(self):
if len(self.memory
) < self.batch_size or self.steps % self.update_steps != 0:
return
#print("fetching minibatch from replay memory")
batch = self.memory.sample(self.batch_size)
(states, actions, reward, next_states, is_terminal) = batch
states = states
next_states = next_states
terminal = FloatTensor([1 if t else 0 for t in is_terminal])
reward = FloatTensor(reward)
batch_index = torch.arange(self.batch_size, dtype=torch.long)
# Current Q Values
_, q_values = self.eval_model.predict_batch(states)
#q_values = q_values[np.arange(self.batch_size), actions]
q_values = q_values[batch_index, actions]
# Calculate target
if self.use_target_model:
#print("target_model.predict")
best_actions, q_next = self.target_model.predict_batch(next_states)
else:
best_actions, q_next = self.eval_model.predict_batch(next_states)
q_max = q_next[batch_index, best_actions]
terminal = 1 - terminal
q_max *= terminal
q_target = reward + self.beta * q_max
# update model
self.eval_model.fit(q_values, q_target)
def learn_and_evaluate(self, training_episodes, test_interval):
test_number = training_episodes // test_interval
all_results = []
for i in range(test_number):
# learn
self.learn(test_interval)
# evaluate
avg_reward = self.evaluate()
all_results.append(avg_reward)
return all_results
def learn(self, test_interval):
for episode in tqdm(range(test_interval), desc="Training"):
state = self.env.reset()
done = False
steps = 0
while steps < self.max_episode_steps and not done:
#INSERT YOUR CODE HERE
# add experience from explore-exploit policy to memory
action = self.explore_or_exploit_policy(state)
next_state, reward, done, info = self.env.step(action)
self.memory.add(state, action, reward, next_state, done)
# update the model every 'update_steps' of experience
self.update_batch()
# update the target network (if the target network is being used) every 'model_replace_freq' of experiences
if self.use_target_model and (self.steps %
self.model_replace_freq == 0):
self.target_model.replace(self.eval_model)
self.steps += 1
steps += 1
state = next_state
def evaluate(self, trials=30):
total_reward = 0
for _ in tqdm(range(trials), desc="Evaluating"):
state = self.env.reset()
done = False
steps = 0
while steps < self.max_episode_steps and not done:
steps += 1
action = self.greedy_policy(state)
state, reward, done, _ = self.env.step(action)
total_reward += reward
avg_reward = total_reward / trials
print(avg_reward)
f = open(result_file, "a+")
f.write(str(avg_reward) + "\n")
f.close()
if avg_reward >= self.best_reward:
self.best_reward = avg_reward
self.save_model()
return avg_reward
# save model
def save_model(self):
self.eval_model.save(result_floder + '/best_model.pt')
# load model
def load_model(self):
self.eval_model.load(result_floder + '/best_model.pt')
class DQN_agent(object):
def __init__(self, env, hyper_params, action_space=len(ACTION_DICT)):
self.env = env
self.max_episode_steps = env._max_episode_steps
self.beta = hyper_params['beta']
self.initial_epsilon = 1
self.final_epsilon = hyper_params['final_epsilon']
self.epsilon_decay_steps = hyper_params['epsilon_decay_steps']
self.episode = 0
self.steps = 0
self.best_reward = 0
self.learning = True
self.action_space = action_space
state = env.reset()
input_len = len(state)
output_len = action_space
self.eval_model = DQNModel(input_len,
output_len,
learning_rate=hyper_params['learning_rate'])
self.use_target_model = hyper_params['use_target_model']
if self.use_target_model:
self.target_model = DQNModel(input_len, output_len)
self.memory = ReplayBuffer(hyper_params['memory_size'])
self.batch_size = hyper_params['batch_size']
self.update_steps = hyper_params['update_steps']
self.model_replace_freq = hyper_params['model_replace_freq']
# Linear decrease function for epsilon
def linear_decrease(self, initial_value, final_value, curr_steps,
final_decay_steps):
decay_rate = curr_steps / final_decay_steps
if decay_rate > 1:
decay_rate = 1
return initial_value - (initial_value - final_value) * decay_rate
def explore_or_exploit_policy(self, state):
p = uniform(0, 1)
# Get decreased epsilon
epsilon = self.linear_decrease(self.initial_epsilon,
self.final_epsilon, self.steps,
self.epsilon_decay_steps)
if p < epsilon:
#return action
return randint(0, self.action_space - 1)
else:
#return action
return self.greedy_policy(state)
def greedy_policy(self, state):
return self.eval_model.predict(state)
def update_batch(self):
if len(self.memory
) < self.batch_size or self.steps % self.update_steps != 0:
return
# 1) Sample a 'batch_size' batch of experiences from the memory.
batch = self.memory.sample(self.batch_size)
(states, actions, reward, next_states, is_terminal) = batch
states = states
next_states = next_states
terminal = FloatTensor([1 if t else 0 for t in is_terminal])
reward = FloatTensor(reward)
batch_index = torch.arange(self.batch_size, dtype=torch.long)
# Current Q Values --- 2) Predict the Q-value from the 'eval_model' based on (states, actions)
_, q_values = self.eval_model.predict_batch(states)
q_values = q_values[batch_index, actions]
# Calculate target --- 3) Predict the Q-value from the 'target model' based on (next_states), and take max of each Q-value vector, Q_max
if self.use_target_model:
actions, q_next = self.target_model.predict_batch(next_states)
else:
actions, q_next = self.eval_model.predict_batch(next_states)
q_next = q_next[batch_index, actions]
q_target = FloatTensor([
reward[index] if is_terminal[index] else reward[index] +
self.beta * q_next[index] for index in range(self.batch_size)
])
# update model
self.eval_model.fit(q_values, q_target)
def learn_and_evaluate(self, training_episodes, test_interval):
test_number = training_episodes // test_interval
all_results = []
for i in range(test_number):
# learn
self.learn(test_interval)
# evaluate
avg_reward = self.evaluate()
all_results.append(avg_reward)
return all_results
def learn(self, test_interval):
for episode in tqdm(range(test_interval), desc="Training"):
state = self.env.reset()
done = False
steps = 0
while steps < self.max_episode_steps and not done:
action = self.explore_or_exploit_policy(state)
next_state, reward, done, _ = self.env.step(action)
# Store history
self.memory.add(state, action, reward, next_state, done)
# Update the model
if self.steps % self.update_steps == 0:
self.update_batch()
# Update the target network if DQN uses it
if self.use_target_model:
if self.steps % self.model_replace_freq == 0:
self.target_model.replace(self.eval_model)
# Update information for the next loop
state = next_state
steps += 1
self.steps += 1
def evaluate(self, trials=30):
total_reward = 0
for _ in tqdm(range(trials), desc="Evaluating"):
state = self.env.reset()
done = False
steps = 0
while steps < self.max_episode_steps and not done:
steps += 1
action = self.greedy_policy(state)
state, reward, done, _ = self.env.step(action)
total_reward += reward
avg_reward = total_reward / trials
print(avg_reward)
f = open(result_file, "a+")
f.write(str(avg_reward) + "\n")
f.close()
if avg_reward >= self.best_reward:
self.best_reward = avg_reward
self.save_model()
return avg_reward
# save model
def save_model(self):
self.eval_model.save(result_floder + '/best_model.pt')
# load model
def load_model(self):
self.eval_model.load(result_floder + '/best_model.pt')
for col in range(3):
if env.board[row, col] == 0:
s += (str(row * 3 + col))
elif env.board[row, col] == 1:
s += 'X'
else:
s += 'O'
if col < 2:
s += '|'
print(s)
e = TTT()
e.reset()
model = DQNModel.load('awesome2.pb')
is_finished = False
output = None
winner = None
while not is_finished:
print_board(e)
user_input = input('\nChoose your move ')
output = e.step(int(user_input))
if not is_finished:
predicted_qs = model.predict(e.board)[0]
for index, q in enumerate(predicted_qs):
print('{}: {}'.format(index, q))
ai_action = model.get_top_action(e.board)
output = e.step(ai_action)