def dqn_argo(param_set: Parameter_Set, max_reward):
# Agentの生成
netWork = Network(action_dim=2)
target_network = Network(action_dim=2)
agent = Agent(network=netWork,
target_network=target_network,
eps_start=param_set.eps_init,
eps_anneal=param_set.eps_anneal,
eps_min=param_set.eps_min,
lr=param_set.lr,
gamma=param_set.gamma)
# Envの生成
env = gym.make('CartPole-v0')
replay_buffer = Replay_Buffer(param_set.cap)
save_reward_list = []
reward_list = []
for i in range(REWARD_SAVE_EVALUATION_SIZE):
save_reward_list.append(0)
for i in range(REWARD_EVALUATION_SIZE):
reward_list.append(0)
# データ集め(何回ゲームをやるか)
for i in range(EPISODE_NUM):
# Envの初期化情報の取得
state = env.reset()
done = False
# エピソード報酬初期化
episode_reward = 0
# 1ゲーム終了させる(Envから終了判定もらう)
while not done:
if i > INIT_EXPLORATION:
# Actionをε-greedyで決める
action = agent.get_action(state)
else:
action = env.action_space.sample()
# Action引数にEnvからS、r,dの情報を引っ張ってくる
next_state, reward, done, info = env.step(action)
# エピソード報酬計算
episode_reward += reward
# ReplayBufferにaddする
replay_buffer.add(state, action, next_state, reward, done)
# StにSt+1を代入（更新処理）
state = next_state
loss = tf.constant(0)
if i > INIT_EXPLORATION:
# ニューラルネットワーク学習
sample = replay_buffer.sample(BATCH_SIZE)
if sample:
loss = agent.update(replay_buffer.sample(BATCH_SIZE))
if i % param_set.q_update == 0:
agent.network_synchronize()
reward_list[i % REWARD_EVALUATION_SIZE] = episode_reward
save_reward_list[i % REWARD_SAVE_EVALUATION_SIZE] = episode_reward
if sum(save_reward_list) / len(save_reward_list) >= max_reward:
print("最高記録更新!!!")
agent.save(SAVE_DIRECTORY + SAVE_FILE)
max_reward = sum(save_reward_list) / len(save_reward_list)
return sum(reward_list) / len(reward_list), max_reward
# render()
# print("Collisions detected by agent(s)", ', '.join(str(a) for a in obs if is_collision(a)))
# break
if done['__all__']: break
# Epsilon decay
if flags.train: eps = max(0.01, flags.epsilon_decay * eps)
# Save some training statistics in their respective deques
tasks_finished = sum(done[i] for i in range(flags.num_agents))
done_window.append(tasks_finished / max(1, flags.num_agents))
collisions_window.append(1. if collision else 0.)
scores_window.append(score / max_steps)
steps_window.append(steps_taken)
# Generate training reports, saving our progress every so often
print(get_report(), end=" ")
if episode % flags.report_interval == 0:
print(get_report(show_time=True))
start_time = time.time()
if flags.train: agent.save(project_root / 'checkpoints', episode, eps)
# Add stats to the tensorboard summary
summary.add_scalar('performance/avg_score', np.mean(scores_window),
episode)
summary.add_scalar('performance/avg_steps', np.mean(steps_window), episode)
summary.add_scalar('performance/completions', np.mean(done_window),
episode)
summary.add_scalar('performance/collisions', np.mean(collisions_window),
episode)
