observation = env.reset() # 環境の初期化
# observation, _, _, _ = env.step(env.action_space.sample())
observation = deepcopy(observation)
agent.observe(observation)
for t in range(250): # n回試行する
# env.render() # 表示
action = agent.act()
observation, reward, done, info = env.step(
action) # アクションを実行した結果の状態、報酬、ゲームをクリアしたかどうか、その他の情報を返す
observation = deepcopy(observation)
agent.observe(observation, reward, done)
if done:
break
# test
agent.training = False
observation = env.reset() # 環境の初期化
agent.observe(observation)
for t in range(250):
# env.render() # 表示
action = agent.act()
observation, reward, done, info = env.step(action)
agent.observe(observation)
# agent.get_reward(reward, done)
if done:
print("Episode {}, maintain {} timesteps".format(episode, t))
result.append(t)
break
agent.training = True
x = np.arange(len(result))
