def loop(
env: gym.Env,
agent: agents.Agent,
num_episodes: int,
epsilon: float,
should_learn: bool,
):
wins = 0
episode = 1
observation = env.reset()
env.render()
while episode <= num_episodes:
if random() < epsilon:
action = env.action_space.sample()
else:
action = agent.act(observation)
new_observation, reward, done, info = env.step(action)
if should_learn:
agent.learn(observation, action, new_observation, reward)
env.render()
print("\tEpisodes:", episode, "\tWins:", wins)
print("\tWin Ratio:", wins / episode)
if done:
observation = env.reset()
env.render()
episode += 1
if reward:
wins += 1
else:
observation = new_observation
env.close()
_agent = Agent((8,), 4)
if os.path.exists(_f_checkpoint):
_agent.net.load_checkpoint(_f_checkpoint)
_writer = SummaryWriter(_d_log)
_is_quit = False
while _episode < _n_games:
_observation = _env.reset()
_done = False
_score = 0.0
while not _done:
_action = _agent.get_action(_observation)
_next_observation, _reward, _done, _info = _env.step(_action)
_score += _reward
_agent.learn(_observation, _reward, _next_observation, _done)
_observation = _next_observation
_rgb = _env.render("rgb_array")
_bgr = cv2.cvtColor(_rgb, cv2.COLOR_RGB2BGR)
cv2.imshow("frame", _bgr)
_key_code = cv2.waitKey(1)
if _key_code in [27, ord('q')]:
_is_quit = True
break
if _is_quit:
break
_scores.append(_score)
_episode += 1
_avg_score = float(np.mean(_scores[-100:]))
if _episode % 500 == 0:
