train_categorical_dqn_gym.py

"""An example of training Categorical DQN against OpenAI Gym Envs.

This script is an example of training a CategoricalDQN agent against OpenAI
Gym envs. Only discrete spaces are supported.

To solve CartPole-v0, run:
    python train_categorical_dqn_gym.py --env CartPole-v0
"""

import argparse
import sys

from chainer import optimizers
import gym

import chainerrl
from chainerrl import experiments
from chainerrl import explorers
from chainerrl import misc
from chainerrl import q_functions
from chainerrl import replay_buffer


def main():
    import logging
    logging.basicConfig(level=logging.DEBUG)

    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='CartPole-v1')
    parser.add_argument('--seed', type=int, default=0)
    parser.add_argument('--gpu', type=int, default=0)
    parser.add_argument('--final-exploration-steps',
                        type=int, default=1000)
    parser.add_argument('--start-epsilon', type=float, default=1.0)
    parser.add_argument('--end-epsilon', type=float, default=0.1)
    parser.add_argument('--demo', action='store_true', default=False)
    parser.add_argument('--load', type=str, default=None)
    parser.add_argument('--steps', type=int, default=10 ** 8)
    parser.add_argument('--prioritized-replay', action='store_true')
    parser.add_argument('--replay-start-size', type=int, default=50)
    parser.add_argument('--target-update-interval', type=int, default=100)
    parser.add_argument('--target-update-method', type=str, default='hard')
    parser.add_argument('--soft-update-tau', type=float, default=1e-2)
    parser.add_argument('--update-interval', type=int, default=1)
    parser.add_argument('--eval-n-runs', type=int, default=100)
    parser.add_argument('--eval-interval', type=int, default=1000)
    parser.add_argument('--n-hidden-channels', type=int, default=12)
    parser.add_argument('--n-hidden-layers', type=int, default=3)
    parser.add_argument('--gamma', type=float, default=0.95)
    parser.add_argument('--minibatch-size', type=int, default=None)
    parser.add_argument('--render-train', action='store_true')
    parser.add_argument('--render-eval', action='store_true')
    parser.add_argument('--monitor', action='store_true')
    parser.add_argument('--reward-scale-factor',
                        type=float, default=1.0)
    args = parser.parse_args()

    # Set a random seed used in ChainerRL
    misc.set_random_seed(args.seed, gpus=(args.gpu,))

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

    def make_env(test):
        env = gym.make(args.env)
        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 = chainerrl.wrappers.CastObservationToFloat32(env)
        if args.monitor:
            env = chainerrl.wrappers.Monitor(env, args.outdir)
        if not test:
            # Scale rewards (and thus returns) to a reasonable range so that
            # training is easier
            env = chainerrl.wrappers.ScaleReward(env, args.reward_scale_factor)
        if ((args.render_eval and test) or
                (args.render_train and not test)):
            env = chainerrl.wrappers.Render(env)
        return env

    env = make_env(test=False)
    timestep_limit = env.spec.max_episode_steps
    obs_size = env.observation_space.low.size
    action_space = env.action_space

    n_atoms = 51
    v_max = 500
    v_min = 0

    n_actions = action_space.n
    q_func = q_functions.DistributionalFCStateQFunctionWithDiscreteAction(
        obs_size, n_actions, n_atoms, v_min, v_max,
        n_hidden_channels=args.n_hidden_channels,
        n_hidden_layers=args.n_hidden_layers)
    # Use epsilon-greedy for exploration
    explorer = explorers.LinearDecayEpsilonGreedy(
        args.start_epsilon, args.end_epsilon, args.final_exploration_steps,
        action_space.sample)

    opt = optimizers.Adam(1e-3)
    opt.setup(q_func)

    rbuf_capacity = 50000  # 5 * 10 ** 5
    if args.minibatch_size is None:
        args.minibatch_size = 32
    if args.prioritized_replay:
        betasteps = (args.steps - args.replay_start_size) \
            // args.update_interval
        rbuf = replay_buffer.PrioritizedReplayBuffer(
            rbuf_capacity, betasteps=betasteps)
    else:
        rbuf = replay_buffer.ReplayBuffer(rbuf_capacity)

    agent = chainerrl.agents.CategoricalDQN(
        q_func, opt, rbuf, gpu=args.gpu, gamma=args.gamma,
        explorer=explorer, replay_start_size=args.replay_start_size,
        target_update_interval=args.target_update_interval,
        update_interval=args.update_interval,
        minibatch_size=args.minibatch_size,
        target_update_method=args.target_update_method,
        soft_update_tau=args.soft_update_tau,
    )

    if args.load:
        agent.load(args.load)

    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_n_steps=None,
            eval_n_episodes=args.eval_n_runs,
            eval_interval=args.eval_interval,
            outdir=args.outdir,
            eval_env=eval_env,
            train_max_episode_len=timestep_limit)


if __name__ == '__main__':
    main()