class Agent(BaseAgent):
'''Deep Trading Agent based on Deep Q Learning'''
'''TODO:
1. play
'''
def __init__(self, sess, logger, config, env):
super(Agent, self).__init__(config, logger)
self.sess = sess
self.logger = logger
self.config = config
params = DeepSenseParams(config)
self.env = env
self.history = History(logger, config)
self.replay_memory = ReplayMemory(logger, config)
with tf.variable_scope(STEPS):
self.step_op = tf.Variable(0, trainable=False, name=STEP)
self.step_input = tf.placeholder('int32', None, name=STEP_INPUT)
self.step_assign_op = self.step_op.assign(self.step_input)
self.build_dqn(params)
@property
def summary_writer(self):
return self._summary_writer
def train(self):
start_step = self.step_op.eval()
num_episodes, self.update_count, ep_reward = 0, 0, 0.
total_reward, self.total_loss, self.total_q = 0., 0., 0.
max_avg_ep_reward = 0
ep_rewards, actions = [], []
self.env.new_random_episode(self.history)
for self.step in tqdm(range(start_step, self.max_step),
ncols=70,
initial=start_step):
if self.step == self.learn_start:
num_episodes, self.update_count, ep_reward = 0, 0, 0.
total_reward, self.total_loss, self.total_q = 0., 0., 0.
ep_rewards, actions = [], []
# 1. predict
action = self.predict(self.history.get())
# 2. act
screen, reward, terminal = self.env.act(action)
# 3. observe
self.observe(screen, reward, action, terminal)
if terminal:
self.env.new_random_episode(self.history)
num_episodes += 1
ep_rewards.append(ep_reward)
ep_reward = 0.
else:
ep_reward += reward
actions.append(action)
total_reward += reward
if self.step >= self.learn_start:
if self.step % self.test_step == self.test_step - 1:
avg_reward = total_reward / self.test_step
avg_loss = self.total_loss / self.update_count
avg_q = self.total_q / self.update_count
try:
max_ep_reward = np.max(ep_rewards)
min_ep_reward = np.min(ep_rewards)
avg_ep_reward = np.mean(ep_rewards)
except:
max_ep_reward, min_ep_reward, avg_ep_reward = 0, 0, 0
message = 'avg_r: %.4f, avg_l: %.6f, avg_q: %3.6f, avg_ep_r: %.4f, max_ep_r: %.4f, min_ep_r: %.4f, # game: %d' \
% (avg_reward, avg_loss, avg_q, avg_ep_reward, max_ep_reward, min_ep_reward, num_game)
print_and_log_message(message, self.logger)
if max_avg_ep_reward * 0.9 <= avg_ep_reward:
self.step_assign_op.eval(
{self.step_input: self.step + 1})
self.save_model(self.step + 1)
max_avg_ep_reward = max(max_avg_ep_reward,
avg_ep_reward)
if self.step > 180:
self.inject_summary(
{
'average.reward':
avg_reward,
'average.loss':
avg_loss,
'average.q':
avg_q,
'episode.max reward':
max_ep_reward,
'episode.min reward':
min_ep_reward,
'episode.avg reward':
avg_ep_reward,
'episode.num of game':
num_game,
'episode.rewards':
ep_rewards,
'episode.actions':
actions,
'training.learning_rate':
self.learning_rate_op.eval(
{self.learning_rate_step: self.step}),
}, self.step)
num_game = 0
total_reward = 0.
self.total_loss = 0.
self.total_q = 0.
self.update_count = 0
ep_reward = 0.
ep_rewards = []
actions = []
def predict(self, s_t, test_ep=None):
ep = test_ep or (self.ep_end +
max(0., (self.ep_start - self.ep_end) \
* (self.ep_end_t - max(0., self.step - self.learn_start)) / self.ep_end_t))
if random.random() < ep:
action = random.randrange(self.env.action_size)
else:
action = self.q.action.eval({self.s_t: [s_t]})[0]
return action
def observe(self, screen, reward, action, terminal):
#clip reward in the range min to max
reward = max(self.min_reward, min(self.max_reward, reward))
self.history.add(screen)
self.replay_memory.add(screen, reward, action, terminal)
if self.step > self.learn_start:
if self.step % self.train_frequency == 0:
self.q_learning_mini_batch()
if self.step % self.target_q_update_step == self.target_q_update_step - 1:
self.update_target_network()
def q_learning_mini_batch(self):
if self.replay_memory.count >= self.replay_memory.history_length:
s_t, action, reward, s_t_plus_1, terminal = self.replay_memory.sample(
)
max_q_t_plus_1 = self.t_q.action.eval({self.t_s_t: s_t_plus_1})
terminal = np.array(terminal) + 0.
target_q = reward + (1 - terminal) * max_q_t_plus_1
_, q_t, loss, avg_q_summary = self.sess.run(
[
self.optimizer, self.q.values, self.loss,
self.q.avg_q_summary
], {
self.target_q: target_q,
self.action: action,
self.s_t: s_t,
self.learning_rate_step: self.step,
})
self.summary_writer.add_summary(avg_q_summary, self.step)
self.total_loss += loss
self.total_q += q_t.mean()
self.update_count += 1
def build_dqn(self, params):
with tf.variable_scope(PREDICTION):
self.s_t = tf.placeholder(dtype=tf.float32,
shape=[
None,
self.replay_memory.history_length,
self.replay_memory.num_channels
])
self.q = DeepSense(params,
self.logger,
self.sess,
self.config,
name=Q_NETWORK)
self.q.build_model(self.s_t)
with tf.variable_scope(TARGET):
self.t_s_t = tf.placeholder(dtype=tf.float32,
shape=[
None,
self.replay_memory.history_length,
self.replay_memory.num_channels
])
self.t_q = DeepSense(params,
self.logger,
self.sess,
self.config,
name=T_Q_NETWORK)
self.t_q.build_model(self.t_s_t, train=False)
with tf.variable_scope(UPDATE_TARGET_NETWORK):
self.q_weights_placeholders = {}
self.t_weights_assign_ops = {}
for name in self.q.weights.keys():
self.q_weights_placeholders[name] = tf.placeholder(
tf.float32, self.q.weights[name].get_shape().as_list())
for name in self.q.weights.keys():
self.t_weights_assign_ops[name] = self.t_q.weights[
name].assign(self.q_weights_placeholders[name])
with tf.variable_scope(TRAINING):
self.target_q = tf.placeholder(tf.float32, [None], name=TARGET_Q)
self.action = tf.placeholder(tf.int64, [None], name=ACTION)
action_one_hot = tf.one_hot(self.action,
self.env.action_size,
1.0,
0.0,
name=ACTION_ONE_HOT)
q_acted = tf.reduce_sum(self.q.values * action_one_hot,
reduction_indices=1,
name=Q_ACTED)
with tf.variable_scope(LOSS):
self.delta = self.target_q - q_acted
self.global_step = tf.Variable(0, trainable=False)
self.loss = tf.reduce_mean(clipped_error(self.delta),
name=LOSS)
with tf.variable_scope(OPTIMIZER):
self.learning_rate_step = tf.placeholder(
tf.int64, None, name=LEARNING_RATE_STEP)
self.learning_rate_op = tf.maximum(
self.learning_rate_minimum,
tf.train.exponential_decay(self.learning_rate,
self.learning_rate_step,
self.learning_rate_decay_step,
self.learning_rate_decay,
staircase=True))
self.optimizer = tf.train.RMSPropOptimizer(
self.learning_rate_op, momentum=0.95,
epsilon=0.01).minimize(self.loss)
with tf.variable_scope(SUMMARY):
scalar_summary_tags = ['average.reward', 'average.loss', 'average.q', \
'episode.max reward', 'episode.min reward', 'episode.avg reward', \
'episode.num of game', 'training.learning_rate']
self.summary_placeholders = {}
self.summary_ops = {}
for tag in scalar_summary_tags:
self.summary_placeholders[tag] = \
tf.placeholder('float32', None, name=tag.replace(' ', '_'))
self.summary_ops[tag] = \
tf.summary.scalar(
name="{}-{}".format(self.env_name, tag),
tensor=self.summary_placeholders[tag]
)
histogram_summary_tags = ['episode.rewards', 'episode.actions']
for tag in histogram_summary_tags:
self.summary_placeholders[tag] = \
tf.placeholder('float32', None, name=tag.replace(' ', '_'))
self.summary_ops[tag] = \
tf.summary.histogram(
name=tag,
self.summary_placeholders[tag]
)
self._summary_writer = tf.summary.FileWriter(
config[TENSORBOARD_LOG_DIR])
self._summary_writer.add_graph(sess.graph)
tf.initialize_all_variables().run()
self._saver = tf.train.Saver(self.q.weights.values + [self.step_op],
max_to_keep=30)
self.load_model()
self.update_target_network()
def update_target_network(self):
for name in self.q.weights.keys():
self.t_weights_assign_ops[name].eval({
self.q_weights_placeholders[name]:
self.q.weights[name].eval()
})
def inject_summary(self, tag_dict, step):
summary_str_lists = self.sess.run(
[self.summary_ops[tag] for tag in tag_dict.keys()], {
self.summary_placeholders[tag]: value
for tag, value in tag_dict.items()
})
for summary_str in summary_str_lists:
self.writer.add_summary(summary_str, self.step)
class DQN:
def __init__(self, config, network, loss, optimizer):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.memory = ReplayMemory(config['REPLAY'])
self.policy_net = network.to(self.device)
self.target_net = network.to(self.device)
self.target_net.load_state_dict(self.policy_net.state_dict())
self.target_net.eval()
self.loss = loss
self.optimizer = optimizer(self.policy_net.parameters(), config['lr'])
self.steps_done = 0
self.config = config
def update(self):
self.target_net.load_state_dict(self.policy_net.state_dict())
def select_action(self, state):
EPS_START, EPS_END, EPS_DECAY, n_actions = self.config[
'EPS_START'], self.config['EPS_END'], self.config[
'EPS_DECAY'], self.config['ACTION_SPACE']
sample = random.random()
eps_threshold = EPS_END + (EPS_START - EPS_END) * \
math.exp(-1. * self.steps_done / EPS_DECAY)
self.steps_done += 1
if sample > eps_threshold:
with torch.no_grad():
# t.max(1) will return largest column value of each row.
# second column on max result is index of where max element was
# found, so we pick action with the larger expected reward.
return self.policy_net(state).max(1)[1].view(1, 1)
else:
return torch.tensor([[random.randrange(n_actions)]],
device=self.device,
dtype=torch.long)
def optimize_model(self):
BATCH_SIZE = self.config['BATCH_SIZE']
if len(self.memory) < BATCH_SIZE:
return
transitions = self.memory.sample(BATCH_SIZE)
# Transpose the batch (see https://stackoverflow.com/a/19343/3343043 for
# detailed explanation). This converts batch-array of Transitions
# to Transition of batch-arrays.
batch = Transition(*zip(*transitions))
# Compute a mask of non-final states and concatenate the batch elements
# (a final state would've been the one after which simulation ended)
non_final_mask = torch.tensor(tuple(
map(lambda s: s is not None, batch.next_state)),
device=self.device,
dtype=torch.bool)
non_final_next_states = torch.cat(
[s for s in batch.next_state if s is not None])
state_batch = torch.cat(batch.state)
action_batch = torch.cat(batch.action)
reward_batch = torch.cat(batch.reward)
# Compute Q(s_t, a) - the model computes Q(s_t), then we select the
# columns of actions taken. These are the actions which would've been taken
# for each batch state according to policy_net
state_action_values = self.policy_net(state_batch).gather(
1, action_batch)
# Compute V(s_{t+1}) for all next states.
# Expected values of actions for non_final_next_states are computed based
# on the "older" target_net; selecting their best reward with max(1)[0].
# This is merged based on the mask, such that we'll have either the expected
# state value or 0 in case the state was final.
next_state_values = torch.zeros(BATCH_SIZE, device=self.device)
next_state_values[non_final_mask] = self.target_net(
non_final_next_states).max(1)[0].detach()
# Compute the expected Q values
GAMMA = self.config['GAMMA']
expected_state_action_values = (next_state_values *
GAMMA) + reward_batch
# Compute Huber loss
loss = self.loss(state_action_values,
expected_state_action_values.unsqueeze(1))
# Optimize the model
self.optimizer.zero_grad()
loss.backward()
for param in self.policy_net.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
class Agent(BaseAgent):
'''Deep Trading Agent based on Deep Q Learning'''
'''TODO:
1. add summary ops
2. timing and logging
3. model saving
4. increment self.step
'''
def __init__(self, sess, logger, config, env):
super(Agent, self).__init__(config)
self.sess = sess
self.logger = logger
self.config = config
params = DeepSenseParams(config)
self.env = env
self.history = History(logger, config)
self.replay_memory = ReplayMemory(logger, config)
with tf.variable_scope(STEPS):
self.step_op = tf.Variable(0, trainable=False, name=STEP)
self.step_input = tf.placeholder('int32', None, name=STEP_INPUT)
self.step_assign_op = self.step_op.assign(self.step_input)
self.build_dqn(params)
def train(self):
start_step = self.step_op.eval()
num_episodes, self.update_count, ep_reward = 0, 0, 0.
total_reward, self.total_loss, self.total_q = 0., 0., 0.
max_avg_ep_reward = 0
ep_rewards, actions = [], []
self.env.new_random_episode(self.history)
for self.step in range(start_step, self.max_step):
if self.step == self.learn_start:
num_episodes, self.update_count, ep_reward = 0, 0, 0.
total_reward, self.total_loss, self.total_q = 0., 0., 0.
ep_rewards, actions = [], []
# 1. predict
action = self.predict(self.history.get())
# 2. act
screen, reward, terminal = self.env.act(action)
# 3. observe
self.observe(screen, reward, action, terminal)
if terminal:
self.env.new_random_episode(self.history)
num_episodes += 1
ep_rewards.append(ep_reward)
ep_reward = 0.
else:
ep_reward += reward
actions.append(action)
total_reward += reward
def predict(self, s_t, test_ep=None):
ep = test_ep or (self.ep_end +
max(0., (self.ep_start - self.ep_end) \
* (self.ep_end_t - max(0., self.step - self.learn_start)) / self.ep_end_t))
if random.random() < ep:
action = random.randrange(self.env.action_size)
else:
action = self.q.action.eval({self.s_t: [s_t]})[0]
return action
def observe(self, screen, reward, action, terminal):
#clip reward in the range min to max
reward = max(self.min_reward, min(self.max_reward, reward))
self.history.add(screen)
self.replay_memory.add(screen, reward, action, terminal)
if self.step > self.learn_start:
if self.step % self.train_frequency == 0:
self.q_learning_mini_batch()
if self.step % self.target_q_update_step == self.target_q_update_step - 1:
self.update_target_network()
def q_learning_mini_batch(self):
if self.replay_memory.count >= self.replay_memory.history_length:
s_t, action, reward, s_t_plus_1, terminal = self.replay_memory.sample(
)
max_q_t_plus_1 = self.t_q.action.eval({self.t_s_t: s_t_plus_1})
terminal = np.array(terminal) + 0.
target_q = reward + (1 - terminal) * max_q_t_plus_1
_, q_t, loss = self.sess.run(
[self.optimizer, self.q.values, self.loss], {
self.target_q: target_q,
self.action: action,
self.s_t: s_t,
self.learning_rate_step: self.step,
})
self.total_loss += loss
self.total_q += q_t.mean()
self.update_count += 1
def build_dqn(self, params):
with tf.variable_scope(PREDICTION):
self.s_t = tf.placeholder(dtype=tf.float32,
shape=[
None,
self.replay_memory.history_length,
self.replay_memory.num_channels
])
self.q = DeepSense(params,
self.logger,
self.sess,
self.config,
name=Q_NETWORK)
self.q.build_model(self.s_t)
with tf.variable_scope(TARGET):
self.t_s_t = tf.placeholder(dtype=tf.float32,
shape=[
None,
self.replay_memory.history_length,
self.replay_memory.num_channels
])
self.t_q = DeepSense(params,
self.logger,
self.sess,
self.config,
name=T_Q_NETWORK)
self.t_q.build_model(self.t_s_t, train=False)
with tf.variable_scope(UPDATE_TARGET_NETWORK):
self.q_weights_placeholders = {}
self.t_weights_assign_ops = {}
for name in self.q.weights.keys():
self.q_weights_placeholders[name] = tf.placeholder(
tf.float32, self.q.weights[name].get_shape().as_list())
for name in self.q.weights.keys():
self.t_weights_assign_ops[name] = self.t_q.weights[
name].assign(self.q_weights_placeholders[name])
with tf.variable_scope(TRAINING):
self.target_q = tf.placeholder(tf.float32, [None], name=TARGET_Q)
self.action = tf.placeholder(tf.int64, [None], name=ACTION)
action_one_hot = tf.one_hot(self.action,
self.env.action_size,
1.0,
0.0,
name=ACTION_ONE_HOT)
q_acted = tf.reduce_sum(self.q.values * action_one_hot,
reduction_indices=1,
name=Q_ACTED)
with tf.variable_scope(LOSS):
self.delta = self.target_q - q_acted
self.global_step = tf.Variable(0, trainable=False)
self.loss = tf.reduce_mean(clipped_error(self.delta),
name=LOSS)
with tf.variable_scope(OPTIMIZER):
self.learning_rate_step = tf.placeholder(
tf.int64, None, name=LEARNING_RATE_STEP)
self.learning_rate_op = tf.maximum(
self.learning_rate_minimum,
tf.train.exponential_decay(self.learning_rate,
self.learning_rate_step,
self.learning_rate_decay_step,
self.learning_rate_decay,
staircase=True))
self.optimizer = tf.train.RMSPropOptimizer(
self.learning_rate_op, momentum=0.95,
epsilon=0.01).minimize(self.loss)
# tf.initialize_all_variables().run()
#initialize the q network and the target network with the same weights
# self.update_target_network()
def update_target_network(self):
for name in self.q.weights.keys():
self.t_weights_assign_ops[name].eval({
self.q_weights_placeholders[name]:
self.q.weights[name].eval()
})
