def main(cfg: omegaconf.DictConfig):
# create the environment
env = atari_wrappers.make_env(cfg.exp.env)
env = gym.wrappers.Monitor(env, "recording/", force=True)
obs = env.reset()
# TensorBoard
writer = SummaryWriter()
writer.add_hparams(flatten_dict(cfg), {})
logger.info('Hyperparams:', cfg)
# create the agent
agent = DQNAgent(env, device=cfg.train.device, summary_writer=writer, cfg=cfg)
n_games = 0
max_mean_40_reward = -sys.maxsize
# Play MAX_N_GAMES games
while n_games < cfg.train.max_episodes:
# act greedly
action = agent.act_eps_greedy(obs)
# one step on the environment
new_obs, reward, done, _ = env.step(action)
# add the environment feedback to the agent
agent.add_env_feedback(obs, action, new_obs, reward, done)
# sample and optimize NB: the agent could wait to have enough memories
agent.sample_and_optimize(cfg.train.batch_size)
obs = new_obs
if done:
n_games += 1
agent.print_info()
agent.reset_stats()
obs = env.reset()
if agent.rewards:
current_mean_40_reward = np.mean(agent.rewards[-40:])
if current_mean_40_reward > max_mean_40_reward:
agent.save_model(cfg.train.best_checkpoint)
writer.close()
summary_writer=writer,
hyperparameters=DQN_HYPERPARAMS)
n_games = 0
n_iter = 0
# Play MAX_N_GAMES games
while n_games < MAX_N_GAMES:
# act greedly
action = agent.act_eps_greedy(obs)
# one step on the environment
new_obs, reward, done, _ = env.step(action)
# add the environment feedback to the agent
agent.add_env_feedback(obs, action, new_obs, reward, done)
# sample and optimize NB: the agent could wait to have enough memories
agent.sample_and_optimize(BATCH_SIZE)
obs = new_obs
if done:
n_games += 1
# print info about the agent and reset the stats
agent.print_info()
agent.reset_stats()
if n_games % TEST_FREQUENCY == 0:
print('Test mean:', utils.test_game(env, agent, 1))
def main():
args = parse_args()
# Overwrite default values
DQN_HYPERPARAMS['epsilon_final'] = args.eps
DQN_HYPERPARAMS['double_DQN'] = args.ddqn
# create the environment
# env = atari_wrappers.make_env(ENV_NAME)
env = atari_wrappers.make_env(args.env_name)
# Create run name with environment name and timestamp of launch
# (and optional tag)
run_name = args.env_name
if args.tag != "":
run_name += f"_{args.tag}"
run_name += "_run_" + datetime.now().strftime("%Y%m%d_%H%M")
if SAVE_VIDEO:
# save the video of the games
# env = gym.wrappers.Monitor(env, "main-"+args.env_name, force=True)
# Save every 50th episode
env = gym.wrappers.Monitor(
env,
"videos/" + args.env_name + "/run_" +
datetime.now().strftime("%Y%m%d_%H%M"), # noqa
video_callable=lambda episode_id: episode_id % 50 == 0)
# TensorBoard
writer = SummaryWriter(log_dir=LOG_DIR+'/'+run_name) \
if SUMMARY_WRITER else None
print('Hyperparams:', DQN_HYPERPARAMS)
# create the agent
agent = DQNAgent(env, DQN_HYPERPARAMS, DEVICE, summary_writer=writer)
n_games = 0
# n_iter = 0
# Play MAX_N_GAMES games
while n_games < MAX_N_GAMES:
obs = env.reset()
done = False
while not done:
# act greedly
action = agent.act_eps_greedy(obs)
# one step on the environment
new_obs, reward, done, _ = env.step(action)
# add the environment feedback to the agent
agent.add_env_feedback(obs, action, new_obs, reward, done)
# sample and optimize NB: the agent could wait to have enough
# memories
agent.sample_and_optimize(BATCH_SIZE)
obs = new_obs
n_games += 1
# print info about the agent and reset the stats
agent.print_info()
agent.reset_stats()
# if n_games % TEST_FREQUENCY == 0:
# print('Test mean:', utils.test_game(env, agent, 1))
writer.close()