terminal)
print(agent.tmp_q_values, np.argmax(agent.tmp_q_values),
agent.enable_actions.index(action_t))
# for log
frame += 1
steps += 1
if steps > warmup:
loss += agent.current_loss
Q_max += np.max(agent.Q_values([state_t]))
# experience replay
# warmup中は学習しない
if steps > warmup:
agent.backword()
if steps % n_update_target_network:
agent.update_target()
print(
"epoch: {:03d}/{:03d} | loss: {:.4f} | Q_max: {:.4f} | total reward: {} | steps: {}"
.format(e, n_epochs - 1, loss / frame, Q_max / frame,
total_reward, steps))
except KeyboardInterrupt:
pass
finally:
# ブラウザーを終了する。
env.driver.quit()
# save model
