while e < n_epochs:
frame = 0
loss = 0.0
Q_max = 0.0
env.reset()
state_t_1, reward_t, terminal = env.observe()
# 終了までアクションして経験を積む
# 経験が一定以上になったら経験から学習する
loops += 1
while not terminal:
state_t = state_t_1
exploration = 0.1 if args.load and not start_replay else agent.exploration
action_t, is_random = agent.select_action([state_t], exploration)
env.execute_action(action_t)
state_t_1, reward_t, terminal = env.observe()
# 楽観的初期値法
# 最初のK回は各アクションに対して同じ報酬を得られたことにする
if is_optimistic_epoch(loops, optimistic_num) and not args.load:
action_t = optimistic_action(env, loops, optimistic)
reward_t = 1 if action_t == 0 else 1
start_replay = False
start_replay = agent.store_experience([state_t], action_t, reward_t,
[state_t_1], terminal)
if start_replay:
do_replay_count += 1
start_date = utils.format(args.start_date)
end_date = utils.format(args.end_date)
env = Env(start_date, end_date)
agent = Agent(env.actions, len(env.columns), env.state_size, args.memory_size)
agent.load_model()
terminal = False
total_frame = 0
max_step = 0
frame = 0
state_t, reward_t, terminal = env.observe()
while not terminal:
action_t, is_random = agent.select_action([state_t], 0.0)
env.execute_action(action_t)
state_t, reward_t, terminal = env.observe()
frame += 1
total_frame += 1
if max_step < env.step:
max_step = env.step
print("frame: %s, total_frame: %s, terminal: %s, action: %s, reward: %s" %
(frame, total_frame, terminal, action_t, reward_t))
backend.clear_session()
print("max_step: %s, score: %s" % (max_step, env.score))
