def test(episodes=20, agent=None, load_path=None, ifrender=False, log=False):
if log:
logger.configure(dir="./log/", format_strs="stdout")
if agent is None:
agent = DQN(num_state=16, num_action=4)
if load_path:
agent.load(load_path)
else:
agent.load()
env = Game2048Env()
score_list = []
highest_list = []
for i in range(episodes):
state, _, done, info = env.reset()
state = log2_shaping(state)
start = time.time()
while True:
action = agent.select_action(state, deterministic=True)
next_state, _, done, info = env.step(action)
next_state = log2_shaping(next_state)
state = next_state
if ifrender:
env.render()
if done:
print(env.Matrix)
if log:
logger.logkv('episode number', i + 1)
logger.logkv('episode reward', info['score'])
logger.logkv('episode steps', info['steps'])
logger.logkv('highest', info['highest'])
logger.dumpkvs()
break
end = time.time()
if log:
print('episode time:{} s\n'.format(end - start))
score_list.append(info['score'])
highest_list.append(info['highest'])
print('mean score:{}, mean highest:{}'.format(np.mean(score_list),
np.mean(highest_list)))
print('max score:{}, max hightest:{}'.format(np.max(score_list),
np.max(highest_list)))
result_info = {
'mean': np.mean(score_list),
'max': np.max(score_list),
'list': score_list
}
print(highest_list)
return result_info
def auto_select():
json = request.get_json()
dict_items = json['items']
direction = json['direction']
action = agent.select_action(log2_shaping(env.Matrix), deterministic=True)
state, reward, done, info = env.step(action)
# print(env.score)
if done:
print(env.Matrix)
res = {
'changed': True,
'items': state.flatten().tolist(),
'lose': done,
'score': env.score,
"best_score": env.best_score
}
# res = {'action': action}
res = jsonify(res)
return res
def train():
episodes = train_episodes
logger.configure(dir="./log/", format_strs="stdout,tensorboard,log")
agent = DQN(num_state=16, num_action=4)
env = Game2048Env()
pf_saver = Perfomance_Saver()
model_saver = Model_Saver(num=10)
eval_max_score = 0
for i in range(episodes):
state, reward, done, info = env.reset()
state = log2_shaping(state)
start = time.time()
loss = None
while True:
if agent.buffer.memory_counter <= agent.memory_capacity:
action = agent.select_action(state, random=True)
else:
action = agent.select_action(state)
next_state, reward, done, info = env.step(action)
next_state = log2_shaping(next_state)
reward = log2_shaping(reward, divide=1)
agent.store_transition(state, action, reward, next_state)
state = next_state
if ifrender:
env.render()
if agent.buffer.memory_counter % agent.train_interval == 0 and agent.buffer.memory_counter > agent.memory_capacity: # 相当于填满后才update
loss = agent.update()
if done:
if i % log_interval == 0:
if loss:
logger.logkv('loss', loss)
logger.logkv('training progress', (i+1) / episodes)
logger.logkv('episode reward', info['score'])
logger.logkv('episode steps', info['steps'])
logger.logkv('highest', info['highest'])
logger.logkv('epsilon', agent.epsilon)
logger.dumpkvs()
loss = None
if i % epsilon_decay_interval == 0: # episilon decay
agent.epsilon_decay(i, episodes)
break
end = time.time()
print('episode time:{} s\n'.format(end - start))
# eval
if i % eval_interval == 0 and i:
eval_info = test(episodes=test_episodes, agent=agent)
average_score, max_score, score_lis = eval_info['mean'], eval_info['max'], eval_info['list']
pf_saver.save(score_lis, info=f'episode:{i}')
if int(average_score) > eval_max_score:
eval_max_score = int(average_score)
name = 'dqn_{}.pkl'.format(int(eval_max_score))
agent.save(name=name)
model_saver.save("./save/" + name)
logger.logkv('eval average score', average_score)
logger.logkv('eval max socre', max_score)
logger.dumpkvs()