sac.py

import argparse

from rlkit.envs import ENVS
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.env_replay_buffer import EnvReplayBuffer
from rlkit.envs.wrappers import NormalizedBoxEnv
from rlkit.launchers.launcher_util import setup_logger
from rlkit.samplers.data_collector import MdpPathCollector
from rlkit.torch.sac.policies import TanhGaussianPolicy, MakeDeterministic
from rlkit.torch.sac.sac import SACTrainer
from rlkit.torch.networks import ConcatMlp
from rlkit.torch.torch_rl_algorithm import TorchBatchRLAlgorithm


def experiment(variant):
    expl_env = NormalizedBoxEnv(ENVS[variant['env_name']]())
    eval_env = NormalizedBoxEnv(ENVS[variant['env_name']]())
    obs_dim = expl_env.observation_space.low.size
    action_dim = eval_env.action_space.low.size

    M = variant['layer_size']
    qf1 = ConcatMlp(
        input_size=obs_dim + action_dim,
        output_size=1,
        hidden_sizes=[M, M],
    )
    qf2 = ConcatMlp(
        input_size=obs_dim + action_dim,
        output_size=1,
        hidden_sizes=[M, M],
    )
    target_qf1 = ConcatMlp(
        input_size=obs_dim + action_dim,
        output_size=1,
        hidden_sizes=[M, M],
    )
    target_qf2 = ConcatMlp(
        input_size=obs_dim + action_dim,
        output_size=1,
        hidden_sizes=[M, M],
    )
    policy = TanhGaussianPolicy(
        obs_dim=obs_dim,
        action_dim=action_dim,
        hidden_sizes=[M, M],
    )
    eval_policy = MakeDeterministic(policy)
    eval_path_collector = MdpPathCollector(
        eval_env,
        eval_policy,
    )
    expl_path_collector = MdpPathCollector(
        expl_env,
        policy,
    )
    replay_buffer = EnvReplayBuffer(
        variant['replay_buffer_size'],
        expl_env,
    )
    trainer = SACTrainer(
        env=eval_env,
        policy=policy,
        qf1=qf1,
        qf2=qf2,
        target_qf1=target_qf1,
        target_qf2=target_qf2,
        **variant['trainer_kwargs']
    )
    algorithm = TorchBatchRLAlgorithm(
        trainer=trainer,
        exploration_env=expl_env,
        evaluation_env=eval_env,
        exploration_data_collector=expl_path_collector,
        evaluation_data_collector=eval_path_collector,
        replay_buffer=replay_buffer,
        **variant['algorithm_kwargs']
    )
    algorithm.to(ptu.device)
    algorithm.train()




if __name__ == "__main__":
    # noinspection PyTypeChecker
    parser = argparse.ArgumentParser(description='SAC-runs')
    parser.add_argument("--env", type=str, default='HalfCheetah-Fwd')
    args = parser.parse_args()

    variant = dict(
        algorithm="SAC",
        version="normal",
        layer_size=256,
        replay_buffer_size=int(1E6),
        env_name=args.env,
        algorithm_kwargs=dict(
            num_epochs=2000,
            num_eval_steps_per_epoch=5000,
            num_trains_per_train_loop=1000,
            num_expl_steps_per_train_loop=1000,
            min_num_steps_before_training=1000,
            max_path_length=1000,
            batch_size=256,
        ),
        trainer_kwargs=dict(
            discount=0.99,
            soft_target_tau=5e-3,
            target_update_period=1,
            policy_lr=3E-4,
            qf_lr=3E-4,
            reward_scale=1,
            use_automatic_entropy_tuning=True,
        ),
    )
    setup_logger(exp_prefix='SAC-' + args.env,
                 variant=variant,
                 text_log_file="debug.log",
                 variant_log_file="variant.json",
                 tabular_log_file="progress.csv",
                 snapshot_mode="gap_and_last",
                 snapshot_gap=200,
                 log_tabular_only=False,
                 log_dir=None,
                 git_infos=None,
                 script_name=None,
                 # **create_log_dir_kwargs
                 base_log_dir='./data',
                 exp_id=0,
                 seed=0)
    ptu.set_gpu_mode(True)  # optionally set the GPU (default=False)
    experiment(variant)