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DoubleDQN.py
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DoubleDQN.py
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# coding=utf-8
import tensorflow as tf
import numpy as np
import os
from algorithm import config
from base.env.market import Market
from checkpoints import CHECKPOINTS_DIR
from base.algorithm.model import BaseRLTFModel
from helper.args_parser import model_launcher_parser
from helper.data_logger import generate_algorithm_logger, generate_market_logger
class Algorithm(BaseRLTFModel):
def __init__(self, session, env, a_space, s_space, **options):
super(Algorithm, self).__init__(session, env, a_space, s_space, **options)
self.buffer = np.zeros((self.buffer_size, self.s_space + 1 + 1 + self.s_space))
self.buffer_length = 0
self.update_q_target_step = 200
self.critic_loss = 0
self._init_input()
self._init_nn()
self._init_op()
self._init_saver()
self._init_summary_writer()
def _init_input(self, *args):
self.s = tf.placeholder(tf.float32, [None, self.s_space])
self.s_next = tf.placeholder(tf.float32, [None, self.s_space])
self.q_next = tf.placeholder(tf.float32, [None, self.a_space])
def _init_nn(self, *args):
self.q_eval = self.__build_critic_nn(self.s, 'q_eval')
self.q_target = self.__build_critic_nn(self.s_next, 'q_target')
def _init_op(self):
with tf.variable_scope('loss'):
self.loss = tf.reduce_mean(tf.squared_difference(self.q_next, self.q_eval))
with tf.variable_scope('train'):
self.train_op = tf.train.RMSPropOptimizer(self.learning_rate).minimize(self.loss)
self.e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_eval')
self.t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_target')
self.update_q_target_op = [tf.assign(t, e) for t, e in zip(self.t_params, self.e_params)]
self.session.run(tf.global_variables_initializer())
def train(self):
# 1. If buffer length is less than buffer size, return.
if self.buffer_length < self.buffer_size:
return
# 2. Update Q-Target if need.
if self.total_step % self.update_q_target_step == 0:
self.session.run(self.update_q_target_op)
# 3. Get transition batch.
s, a, r, s_next = self.get_transition_batch()
# 4. Calculate q_eval_next.
q_eval_next = self.session.run(self.q_eval, {self.s: s_next})
# 5. Get action indices and make batch indices.
a_indices = np.argmax(q_eval_next, axis=1)
b_indices = np.arange(self.batch_size, dtype=np.int)
# 6. Calculate q_target_next selected by actions.
q_target_next = self.session.run(self.q_target, {self.s_next: s_next})
q_target_next_with_a = q_target_next[b_indices, a_indices]
# 7. Calculate labels.
q_eval = self.session.run(self.q_eval, {self.s: s})
q_next = q_eval.copy()
q_next[b_indices, a.astype(np.int)] = r + self.gamma * q_target_next_with_a
# 8. Calculate loss.
_, self.critic_loss = self.session.run([self.train_op, self.loss], {self.s: s, self.q_next: q_next})
# 9. Increase total step.
self.total_step += 1
def predict(self, s):
q = self.session.run(self.q_eval, {self.s: s})
a = np.argmax(q)
return self.get_stock_code_and_action(a, use_greedy=True, use_prob=True if self.mode == 'train' else False)
def save_transition(self, s, a, r, s_next):
transition = np.hstack((s, [[a]], [[r]], s_next))
self.buffer[self.buffer_length % self.buffer_size, :] = transition
self.buffer_length += 1
def get_transition_batch(self):
indices = np.random.choice(self.buffer_size, size=self.batch_size)
batch = self.buffer[indices, :]
s = batch[:, :self.s_space]
a = batch[:, self.s_space: self.s_space + 1]
r = batch[:, -self.s_space - 1: -self.s_space]
s_next = batch[:, -self.s_space:]
return s, a, r, s_next
def run(self):
if self.mode != 'train':
self.restore()
else:
for episode in range(self.episodes):
self.log_loss(episode)
s = self.env.reset(self.mode)
while True:
c, a, a_index = self.predict(s)
s_next, r, status, info = self.env.forward(c, a)
self.save_transition(s, a_index, r, s_next)
self.train()
s = s_next
if status == self.env.Done:
self.env.trader.log_asset(episode)
break
if self.enable_saver and episode % 10 == 0:
self.save(episode)
def log_loss(self, episode):
self.logger.warning("Episode: {0} | Critic Loss: {1:.2f}".format(episode, self.critic_loss))
def __build_critic_nn(self, s, scope):
w_init, b_init = tf.random_normal_initializer(.0, .3), tf.constant_initializer(.1)
with tf.variable_scope(scope):
s_first_dense = tf.layers.dense(s,
32,
activation=tf.nn.relu,
kernel_initializer=w_init,
bias_initializer=b_init)
s_second_dense = tf.layers.dense(s_first_dense,
32,
tf.nn.relu,
kernel_initializer=w_init,
bias_initializer=b_init)
q = tf.layers.dense(s_second_dense,
self.a_space,
kernel_initializer=w_init,
bias_initializer=b_init)
return q
def main(args):
mode = args.mode
# mode = 'test'
codes = args.codes
# codes = ["AU88", "RB88", "CU88", "AL88"]
# codes = ["T9999"]
market = args.market
# market = 'future'
episode = args.episode
# episode = 2000
# training_data_ratio = 0.5
training_data_ratio = args.training_data_ratio
model_name = os.path.basename(__file__).split('.')[0]
env = Market(codes, start_date="2012-01-01", end_date="2018-01-01", **{
"market": market,
# "use_sequence": True,
"logger": generate_market_logger(model_name),
"training_data_ratio": training_data_ratio,
})
algorithm = Algorithm(tf.Session(config=config), env, env.trader.action_space, env.data_dim, **{
"mode": mode,
"episodes": episode,
"enable_saver": True,
"learning_rate": 0.003,
"enable_summary_writer": True,
"logger": generate_algorithm_logger(model_name),
"save_path": os.path.join(CHECKPOINTS_DIR, "RL", model_name, market, "model"),
"summary_path": os.path.join(CHECKPOINTS_DIR, "RL", model_name, market, "summary"),
})
algorithm.run()
algorithm.eval()
algorithm.plot()
if __name__ == '__main__':
main(model_launcher_parser.parse_args())