train_td3.py

"""A training script of TD3 on OpenAI Gym Mujoco environments.

This script follows the settings of http://arxiv.org/abs/1802.09477 as much
as possible.
"""

import argparse
import logging
import sys

import gym
import gym.wrappers
import numpy as np
import torch
from torch import nn

import pfrl
from pfrl import experiments
from pfrl import explorers
from pfrl import utils
from pfrl import replay_buffers


def main():

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--outdir",
        type=str,
        default="results",
        help=(
            "Directory path to save output files."
            " If it does not exist, it will be created."
        ),
    )
    parser.add_argument(
        "--env",
        type=str,
        default="Hopper-v2",
        help="OpenAI Gym MuJoCo env to perform algorithm on.",
    )
    parser.add_argument("--seed", type=int, default=0, help="Random seed [0, 2 ** 32)")
    parser.add_argument(
        "--gpu", type=int, default=0, help="GPU to use, set to -1 if no GPU."
    )
    parser.add_argument(
        "--load", type=str, default="", help="Directory to load agent from."
    )
    parser.add_argument(
        "--steps",
        type=int,
        default=10 ** 6,
        help="Total number of timesteps to train the agent.",
    )
    parser.add_argument(
        "--eval-n-runs",
        type=int,
        default=10,
        help="Number of episodes run for each evaluation.",
    )
    parser.add_argument(
        "--eval-interval",
        type=int,
        default=5000,
        help="Interval in timesteps between evaluations.",
    )
    parser.add_argument(
        "--replay-start-size",
        type=int,
        default=10000,
        help="Minimum replay buffer size before " + "performing gradient updates.",
    )
    parser.add_argument("--batch-size", type=int, default=100, help="Minibatch size")
    parser.add_argument(
        "--render", action="store_true", help="Render env states in a GUI window."
    )
    parser.add_argument(
        "--demo", action="store_true", help="Just run evaluation, not training."
    )
    parser.add_argument("--load-pretrained", action="store_true", default=False)
    parser.add_argument(
        "--pretrained-type", type=str, default="best", choices=["best", "final"]
    )
    parser.add_argument(
        "--monitor", action="store_true", help="Wrap env with gym.wrappers.Monitor."
    )
    parser.add_argument(
        "--log-level", type=int, default=logging.INFO, help="Level of the root logger."
    )
    args = parser.parse_args()

    logging.basicConfig(level=args.log_level)

    args.outdir = experiments.prepare_output_dir(args, args.outdir, argv=sys.argv)
    print("Output files are saved in {}".format(args.outdir))

    # Set a random seed used in PFRL
    utils.set_random_seed(args.seed)

    def make_env(test):
        env = gym.make(args.env)
        # Unwrap TimeLimit wrapper
        assert isinstance(env, gym.wrappers.TimeLimit)
        env = env.env
        # Use different random seeds for train and test envs
        env_seed = 2 ** 32 - 1 - args.seed if test else args.seed
        env.seed(env_seed)
        # Cast observations to float32 because our model uses float32
        env = pfrl.wrappers.CastObservationToFloat32(env)
        if args.monitor:
            env = pfrl.wrappers.Monitor(env, args.outdir)
        if args.render and not test:
            env = pfrl.wrappers.Render(env)
        return env

    env = make_env(test=False)
    timestep_limit = env.spec.max_episode_steps
    obs_space = env.observation_space
    action_space = env.action_space
    print("Observation space:", obs_space)
    print("Action space:", action_space)

    obs_size = obs_space.low.size
    action_size = action_space.low.size

    policy = nn.Sequential(
        nn.Linear(obs_size, 400),
        nn.ReLU(),
        nn.Linear(400, 300),
        nn.ReLU(),
        nn.Linear(300, action_size),
        nn.Tanh(),
        pfrl.policies.DeterministicHead(),
    )
    policy_optimizer = torch.optim.Adam(policy.parameters())

    def make_q_func_with_optimizer():
        q_func = nn.Sequential(
            pfrl.nn.ConcatObsAndAction(),
            nn.Linear(obs_size + action_size, 400),
            nn.ReLU(),
            nn.Linear(400, 300),
            nn.ReLU(),
            nn.Linear(300, 1),
        )
        q_func_optimizer = torch.optim.Adam(q_func.parameters())
        return q_func, q_func_optimizer

    q_func1, q_func1_optimizer = make_q_func_with_optimizer()
    q_func2, q_func2_optimizer = make_q_func_with_optimizer()

    rbuf = replay_buffers.ReplayBuffer(10 ** 6)

    explorer = explorers.AdditiveGaussian(
        scale=0.1, low=action_space.low, high=action_space.high
    )

    def burnin_action_func():
        """Select random actions until model is updated one or more times."""
        return np.random.uniform(action_space.low, action_space.high).astype(np.float32)

    # Hyperparameters in http://arxiv.org/abs/1802.09477
    agent = pfrl.agents.TD3(
        policy,
        q_func1,
        q_func2,
        policy_optimizer,
        q_func1_optimizer,
        q_func2_optimizer,
        rbuf,
        gamma=0.99,
        soft_update_tau=5e-3,
        explorer=explorer,
        replay_start_size=args.replay_start_size,
        gpu=args.gpu,
        minibatch_size=args.batch_size,
        burnin_action_func=burnin_action_func,
    )

    if len(args.load) > 0 or args.load_pretrained:
        if args.load_pretrained:
            raise Exception("Pretrained models are currently unsupported.")
        # either load or load_pretrained must be false
        assert not len(args.load) > 0 or not args.load_pretrained
        if len(args.load) > 0:
            agent.load(args.load)
        else:
            agent.load(
                utils.download_model("TD3", args.env, model_type=args.pretrained_type)[
                    0
                ]
            )

    eval_env = make_env(test=True)
    if args.demo:
        eval_stats = experiments.eval_performance(
            env=eval_env,
            agent=agent,
            n_steps=None,
            n_episodes=args.eval_n_runs,
            max_episode_len=timestep_limit,
        )
        print(
            "n_runs: {} mean: {} median: {} stdev {}".format(
                args.eval_n_runs,
                eval_stats["mean"],
                eval_stats["median"],
                eval_stats["stdev"],
            )
        )
    else:
        experiments.train_agent_with_evaluation(
            agent=agent,
            env=env,
            steps=args.steps,
            eval_env=eval_env,
            eval_n_steps=None,
            eval_n_episodes=args.eval_n_runs,
            eval_interval=args.eval_interval,
            outdir=args.outdir,
            train_max_episode_len=timestep_limit,
        )


if __name__ == "__main__":
    main()