def ppo_pixel(log_name='ppo-dmlab-image', render=False):
config = Config()
log_dir = get_default_log_dir(ppo_pixel.__name__)
config.num_workers = 8
config.task_fn = lambda: Task(
use_vision=True,
use_pos=False,
num_envs=config.num_workers,
render=render,
)
config.eval_env = Task(
use_vision=True,
use_pos=False,
num_envs=config.num_workers,
log_dir=log_dir,
render=render,
)
config.optimizer_fn = lambda params: torch.optim.RMSprop(
params, lr=0.00025, alpha=0.99, eps=1e-5)
config.network_fn = lambda: CategoricalActorCriticNet(
config.state_dim, config.action_dim, NatureConvBody(in_channels=3))
config.state_normalizer = ImageNormalizer()
config.reward_normalizer = SignNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.95
config.entropy_weight = 0.01
config.gradient_clip = 0.5
config.rollout_length = 128
config.optimization_epochs = 3
config.mini_batch_size = 32 * 8
config.ppo_ratio_clip = 0.1
config.log_interval = 128 * 8
config.logger = get_logger(tag=log_name)
config.tag = log_name # this name must be unique. Anything with the same name will be overwritten
config.max_steps = int(2e7)
run_steps(PPOAgent(config))