class Model_Server(object):
def __init__(self, env, hyper_params, memory_server):
"""
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
"""
self.beta = hyper_params['beta']
state = env.reset()
action_space = len(ACTION_DICT)
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_server = memory_server
def update_batch(self, batch_size):
batch = ray.get(self.memory_server.sample.remote(batch_size))
(states, actions, reward, next_states, is_terminal) = batch
states = states
next_states = next_states
terminal = FloatTensor([0 if t else 1 for t in is_terminal])
reward = FloatTensor(reward)
batch_index = torch.arange(batch_size, dtype=torch.long)
# Current Q Values
_, q_values = self.eval_model.predict_batch(states)
q_values = q_values[batch_index, actions]
# Calculate target
if self.use_target_model:
actions, q_next = self.target_model.predict_batch(next_states)
q_next = q_next[batch_index, actions]
else:
actions, q_next = self.eval_model.predict_batch(next_states)
q_next = q_next[batch_index, actions]
#INSERT YOUR CODE HERE --- neet to compute 'q_targets' used below
q_max = q_next * terminal
q_target = reward + self.beta * q_max
# update model
self.eval_model.fit(q_values, q_target)
def replace_target(self):
self.target_model.replace(self.eval_model)
def greedy_policy(self, state):
return self.eval_model.predict(state)
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_Model_Server():
def __init__(self, env, hyper_params, batch_size, update_steps, memory_size, beta, model_replace_freq,
learning_rate, use_target_model=True, memory=Memory_Server, action_space=2,
training_episodes=7000, test_interval=50):
# super().__init__(update_steps, memory_size, model_replace_freq, learning_rate, beta=0.99, batch_size = 32, use_target_model=True)
self.batch_size = batch_size
state = env.reset()
input_len = len(state)
output_len = action_space
self.eval_model = DQNModel(input_len, output_len, learning_rate=0.0003)
self.target_model = DQNModel(input_len, output_len)
self.steps = 0
self.memory = memory
# self.memory = ReplayBuffer(hyper_params['memory_size'])
self.prev = 0
self.next = 0
self.model_dq = deque()
self.result = [0] * ((training_episodes // test_interval) + 1)
self.previous_q_networks = []
self.result_count = 0
self.learning_episodes = training_episodes
self.episode = 0
self.is_collection_completed = False
self.evaluator_done = False
self.batch_num = training_episodes // test_interval
self.use_target_model = True
self.beta = 0.99
self.test_interval = test_interval
def get_evaluation_model(self):
if self.episode >= self.learning_episodes:
self.is_collection_completed = True
return self.is_collection_completed
def replace(self):
self.target_model.replace(self.eval_model)
def get_total_steps(self):
return self.steps
def predict_next(self, state, e_model):
return e_model.predict(state)
def get_predict(self, state):
return self.eval_model.predict(state)
def set_collect_count(self):
self.next += 1
def set_collector_count(self):
self.episode += 1
def get_evaluation_count(self):
return self.result_count
def get_evaluator_count(self):
return self.episode
def ask_evaluation(self):
if len(self.previous_q_networks) > self.result_count:
num = self.result_count
evluation_q_network = self.previous_q_networks[num]
self.result_count += 1
self.episode += 50
return evluation_q_network, False, num
else:
if self.episode >= self.learning_episodes:
self.evaluator_done = True
return [], self.evaluator_done, None
def update_batch(self):
self.steps += 10
if ray.get(self.memory.__len__.remote()) < self.batch_size: # or self.steps % self.update_steps != 0:
return
if self.is_collection_completed:
return
batch = ray.get(self.memory.sample.remote(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)
_, q_values = self.eval_model.predict_batch(states)
q_values = q_values[batch_index, actions]
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)#dont need though
q_targets = []
for i in range(0, len(terminal), 1):
if terminal[i] == 1:
q_targets.append(reward[i])
else:
q_targets.append(reward[i] + (self.beta * torch.max(q_next, 1).values[i].data))
q_target = FloatTensor(q_targets)
self.eval_model.fit(q_values, q_target)
if self.episode // self.test_interval + 1 > len(self.previous_q_networks):
model_id = ray.put(self.eval_model)
self.previous_q_networks.append(model_id)
return self.steps
def set_results(self, result, num):
self.result[num] = result
def get_results(self):
return self.result
class DQN_server(object):
def __init__(self,
learning_rate,
training_episodes,
memory,
env,
test_interval=50,
batch_size=32,
action_space=len(ACTION_DICT),
beta=0.99):
self.env = env
#self.max_episode_steps = env._max_episode_steps
self.batch_num = training_episodes // test_interval
self.steps = 0
self.collector_done = False
self.evaluator_done = False
self.training_episodes = training_episodes
self.episode = 0
#self.esults = []
self.batch_size = batch_size
self.privous_q_model = []
self.results = [0] * (self.batch_num + 1)
self.result_count = 0
self.memory = memory
self.use_target_model = True
state = env.reset()
input_len = len(state)
output_len = action_space
self.eval_model = DQNModel(input_len, output_len, learning_rate=0.0003)
self.target_model = DQNModel(input_len, output_len)
self.batch_size = hyper_params['batch_size']
self.update_steps = hyper_params['update_steps']
self.model_replace_freq = hyper_params['model_replace_freq']
def get_eval_model(self):
print(self.episode)
if self.episode >= self.training_episodes:
self.collector_done = True
return self.collector_done
def add_episode(self):
self.episode += 1
return self.episode
def update_batch(self):
if self.collector_done:
return
if ray.get(self.memory.__len__.remote()
) < self.batch_size or self.steps % self.update_steps != 0:
return
batch = ray.get(self.memory.sample.remote(self.batch_size))
(states, actions, reward, next_states, is_terminal) = batch
self.steps += self.update_steps
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[batch_index, actions]
# Calculate target
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)
#INSERT YOUR CODE HERE --- neet to compute 'q_targets' used below
q_targets = [0] * self.batch_size
for i in range(self.batch_size):
if terminal[i] == 1:
q_targets[i] = reward[i]
else:
max_value = torch.max(q_next, dim=1).values[i].data
q_targets[i] = reward[i] + beta * max_value
q_target = FloatTensor(q_targets)
# update model
self.eval_model.fit(q_values, q_target)
if self.episode // test_interval + 1 > len(self.privous_q_model):
model_id = ray.put(self.eval_model)
self.privous_q_model.append(model_id)
return self.steps
# evalutor
def add_result(self, result, num):
#print(num)
self.results[num] = result
def get_results(self):
return self.results
def ask_evaluation(self):
if len(self.privous_q_model) > self.result_count:
num = self.result_count
evluation_q_model = self.privous_q_model[num]
self.result_count += 1
return evluation_q_model, False, num
else:
if self.episode >= self.training_episodes:
self.evaluator_done = True
return [], self.evaluator_done, None
def replace(self):
self.target_model.replace(self.eval_model)
def predict(self, state):
return self.eval_model.predict(state)
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):
pass
def learn(self):
pass
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
"""
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']
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)
class Model_Server():
def __init__(self, env, hyper_params, memory, action_space):
self.epsilon_decay_steps = hyper_params['epsilon_decay_steps']
self.final_epsilon = hyper_params['final_epsilon']
self.batch_size = hyper_params['batch_size']
self.update_steps = hyper_params['update_steps']
self.beta = hyper_params['beta']
self.model_replace_freq = hyper_params['model_replace_freq']
self.learning_rate = hyper_params['learning_rate']
self.training_episodes = hyper_params['training_episodes']
self.test_interval = hyper_params['test_interval']
self.memory = memory
self.episode = 0
self.steps = 0
self.result_count = 0
self.next = 0
self.batch_num = self.training_episodes // self.test_interval
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.target_model = DQNModel(input_len, output_len)
self.results = [0] * (self.batch_num + 1)
self.previous_q_networks = []
self.collector_done = False
self.evaluator_done = False
def ask_evaluation(self):
if len(self.previous_q_networks) > self.result_count:
num = self.result_count
evaluation_q_network = self.previous_q_networks[num]
self.result_count += 1
return evaluation_q_network, False, num
else:
if self.episode >= self.training_episodes:
self.evaluator_done = True
return [], self.evaluator_done, None
def get_evaluation_model(self):
if self.episode >= self.training_episodes:
self.collector_done = True
return self.eval_model, self.collector_done
def replace_with_eval_model(self):
self.target_model.replace(self.eval_model)
def get_model_steps(self):
return self.steps
def predict_next_eval(self, state, eval_model):
return eval_model.predict(state)
def get_predict(self, state):
return self.eval_model.predict(state)
def increment_episode(self):
self.episode += 1
def increment_model_steps(self):
self.steps += 1
return self.steps
def update_batch(self):
self.steps += self.update_steps
if ray.get(self.memory.__len__.remote()) < self.batch_size: # or self.steps % self.update_steps != 0:
return
if self.collector_done:
return
batch = ray.get(self.memory.sample.remote(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[batch_index, actions]
# Calculate target
actions, q_next = self.target_model.predict_batch(next_states)
q_max, indices = torch.max(q_next, dim=1)
# INSERT YOUR CODE HERE --- neet to compute 'q_targets' used below
q_targets = []
for i, is_term in enumerate(terminal):
if is_term == 1:
q_targets.append(reward[i])
else:
q_targets.append(reward[i] + self.beta * q_max[i])
q_targets_tensor = FloatTensor(q_targets)
# update model
self.eval_model.fit(q_values, q_targets_tensor)
if self.episode // self.test_interval + 1 > len(self.previous_q_networks):
model_id = ray.put(self.eval_model)
self.previous_q_networks.append(model_id)
return self.steps
def add_result(self, reward, num):
self.results[num] = reward
def get_results(self):
return self.results
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')
class DQN_model_server(object):
def __init__(self, env, memory, action_space=2, test_interval=50):
self.collector_done = False
self.evaluator_done = False
self.env = env
# self.max_episode_steps = env._max_episode_steps
self.max_episode_steps = 200
self.beta = hyperparams_CartPole['beta']
self.initial_epsilon = 1
self.final_epsilon = hyperparams_CartPole['final_epsilon']
self.epsilon_decay_steps = hyperparams_CartPole['epsilon_decay_steps']
self.batch_size = hyperparams_CartPole['batch_size']
self.episode = 0
self.steps = 0
self.best_reward = 0
self.learning = True
self.action_space = action_space
self.previous_q_models = []
self.results = [0] * (self.batch_size + 1)
self.reuslt_count = 0
self.episode = 0
self.test_interval = test_interval
self.memory = memory
state = env.reset()
input_len = len(state)
output_len = action_space
self.eval_model = DQNModel(input_len, output_len, learning_rate=hyperparams_CartPole['learning_rate'])
self.use_target_model = hyperparams_CartPole['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'])
self.batch_size = hyperparams_CartPole['batch_size']
self.update_steps = hyperparams_CartPole['update_steps']
self.model_replace_freq = hyperparams_CartPole['model_replace_freq']
def get_steps(self):
return self.steps
def update_batch(self):
# if len(memory) < self.batch_size or self.steps % self.update_steps != 0:
# return
# print(len(self.memory.remote()))
batch = self.memory.sample.remote(self.batch_size)
(states, actions, reward, next_states, is_terminal) = ray.get(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
_, self.q_values = self.eval_model.predict_batch(states)
self.q_values = self.q_values[batch_index, actions]
# Calculate target
if self.use_target_model:
actions, self.q_next = self.target_model.predict_batch(next_states)
self.q_next = self.q_next[batch_index, actions]
else:
actions, self.q_next = self.eval_model.predict_batch(next_states)
self.q_next = self.q_next[batch_index, actions]
# INSERT YOUR CODE HERE --- neet to compute 'q_targets' used below
self.q_target = []
for i in range(len(reward)):
if terminal[i] == 1:
self.q_target.append(reward[i])
else:
self.q_target.append(reward[i] + self.beta * self.q_next[i])
self.q_target = FloatTensor(self.q_target)
# update model
self.eval_model.fit(self.q_values, self.q_target)
if(np.random.randint(100)==4):
print("==========",self.q_values[0],self.q_target[0])
# print("..................................................", self.evaluate())
# score = self.evaluate()
# f_results = open("./results_8_4.txt", "a+")
# f_results.write(str(score) + "\n")
# f_results.close()
if self.episode // self.test_interval + 1 > len(self.previous_q_models):
model_id = ray.put(self.eval_model)
self.previous_q_models.append(model_id)
self.steps += 10
return self.steps
def greedy_policy(self, state):
return self.eval_model.predict(state)
def replace_target(self):
return self.target_model.replace(self.eval_model)
# evalutor
# def add_result(self, result):
# self.results[num] = result
def get_reuslts(self):
return self.results
def ask_evaluation(self):
if len(self.previous_q_models) > self.reuslt_count:
num = self.reuslt_count
evluation_q_model = self.previous_q_models[num]
self.reuslt_count += 1
return evluation_q_model, False, num
else:
if self.episode >= training_episodes:
self.evaluator_done = True
return [], self.evaluator_done, None
def add_episode(self):
self.episode += 1
class RLAgent_model_server():
def __init__(self, env, hyper_params, memo_server):
self.memory_server = memo_server
self.env = env
self.max_episode_steps = env._max_episode_steps
self.beta = hyper_params['beta']
self.training_episodes = hyper_params['training_episodes']
self.test_interval = hyper_params['test_interval']
action_space = len(ACTION_DICT)
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.batch_size = hyper_params['batch_size']
self.update_steps = hyper_params['update_steps']
self.model_replace_freq = hyper_params['model_replace_freq']
self.collector_done = False
self.results = []
self.initial_epsilon = 1
self.final_epsilon = hyper_params['final_epsilon']
self.epsilon_decay_steps = hyper_params['epsilon_decay_steps']
self.replace_targe_cnt = 0
self.epsilon = 1
self.eval_models_seq = 1
def update_batch(self):
# Get memory sample
batch = ray.get(self.memory_server.sample.remote(self.batch_size))
if not batch:
return
(states, actions, reward, next_states, is_terminal) = batch
# Setting torch value
states = states
next_states = next_states
terminal = FloatTensor([0 if t else 1 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[batch_index, actions]
# Calculate target
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)
max_q_next, index = torch.max(q_next, dim=1)
q_target = reward + self.beta * max_q_next * terminal
# Update model
self.eval_model.fit(q_values, q_target)
def replace_target_model(self):
if self.use_target_model and self.steps % self.model_replace_freq == 0:
self.target_model.replace(self.eval_model)
def evaluate_result(self):
# print(self.episode, self.training_episodes)
self.episode += 1
if self.episode % self.test_interval == 0:
self.save_model()
# evaluation_worker_gg.remote(self.env, self.memory_server, self.eval_model, self.test_interval)
def save_model(self):
filename = "/best_model{0}.pt".format(self.eval_models_seq)
self.eval_model.save(result_floder + filename)
self.memory_server.add_evamodel_dir.remote(result_floder + filename)
self.eval_models_seq += 1
def ask_evaluate(self):
if len(self.eval_models) == 0:
return None, self.episode >= self.training_episodes
eval_model, is_done = self.eval_models[0]
del self.eval_models[0]
return eval_model, is_done
def get_collector_done(self):
return self.episode >= self.training_episodes
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):
self.epsilon = self.linear_decrease(self.initial_epsilon,
self.final_epsilon,
self.steps,
self.epsilon_decay_steps)
return randint(0, self.action_space - 1) if uniform(0, 1) < self.epsilon else self.greedy_policy(state)
def greedy_policy(self, state):
return self.eval_model.predict(state)
def add_results(self, result):
self.results.append(result)
def get_reuslts(self):
return self.results
def update_and_replace_model(self):
self.steps += 1
if self.steps % self.update_steps != 0:
self.update_batch()
self.replace_target_model()