class Agent:
def __init__(self, state_size, action_size, sample_num):
sess = tf.Session()
self.policy = Policy(sess, state_size, action_size, sample_num)
self.state_batch = []
self.action_batch = []
self.reward_list = []
self.step_list = []
self.weight_bach = []
self.sample_num = sample_num
sess.run(tf.global_variables_initializer())
def choose_action(self, state):
return self.policy.choose_action(state)
def store(self, state, action, reward):
self.state_batch.append(state)
self.action_batch.append(action)
self.reward_list.append(reward)
def train(self):
state_batch = np.vstack(self.state_batch)
action_batch = np.vstack(self.action_batch)
t = 0
for i in range(self.sample_num):
tlast = t + self.step_list[i]
for _ in range(self.step_list[i]):
weight = 0.0
for n in range(t, tlast):
weight += self.reward_list[n] * np.power(gamma, (n - t))
self.weight_bach.append(weight)
t += 1
weight_bach = np.vstack(self.weight_bach)
self.policy.train(state_batch, action_batch, weight_bach)
self.state_batch = []
self.action_batch = []
self.reward_list = []
self.step_list = []
self.weight_bach = []
else:
render = False
if episode % SHOW_INFOS == 1:
show_infos(episode)
init_game()
over = False
if render:
show_render()
while not over:
action = policy.choose_action()
env.apply_action(action)
over = is_over()
policy.update_replay_memory(over)
policy.train(over)
steps_remaining -= 1
if render:
show_render()
if policy.END_EPSILON_DECAYING >= episode >= policy.START_EPSILON_DECAYING:
policy.epsilon -= policy.epsilon_decay_value
policy.test_model()
