class Agent(object):
def __init__(self):
self.agent = Framework()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
self.saver = tf.train.Saver()
self.sess.run(tf.global_variables_initializer())
self.sess.graph.finalize()
def get_deterministic_policy(self, inputs):
return self.agent.get_deterministic_policy(self.sess, inputs)
def get_stochastic_policy(self, inputs, epsilon=0.9):
return self.agent.get_stochastic_policy(self.sess, inputs, epsilon)
def update_cache(self, state, action, reward, next_state, done):
self.agent.update_cache(state, action, reward, next_state, done)
def update_eval(self):
self.agent.update_value_net(self.sess)
def update_target(self):
self.agent.update_target_net(self.sess)
def save_model(self, path="model/ddqn.ckpt"):
self.saver.save(self.sess, path)
def restore_model(self, path="model/ddqn.ckpt"):
self.saver.restore(self.sess, path)
def close(self):
self.sess.close()
import numpy as np
import tensorflow as tf
from agent.framework import Framework
from emulator_v0.main import Account
A = Account()
F = Framework()
# print(len(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)))
state, universe = A.reset()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
order = F.get_deterministic_policy(sess, state)
next_state, next_universe, reward, done, value, portfolio = \
A.step(order, universe)
for i in range(2048):
F.update_cache(state, order, reward, next_state, done)
F.update_value_net(sess)