train_a3c_ale.py

import argparse
import os

# Prevent numpy from using multiple threads
os.environ['OMP_NUM_THREADS'] = '1'  # NOQA

import chainer
from chainer import links as L
import gym
import gym.wrappers
import numpy as np

import chainerrl
from chainerrl.agents import a3c
from chainerrl import experiments
from chainerrl import links
from chainerrl import misc
from chainerrl.optimizers.nonbias_weight_decay import NonbiasWeightDecay
from chainerrl.optimizers import rmsprop_async
from chainerrl import policy
from chainerrl.recurrent import RecurrentChainMixin
from chainerrl import v_function

from chainerrl.wrappers import atari_wrappers


class A3CFF(chainer.ChainList, a3c.A3CModel):

    def __init__(self, n_actions):
        self.head = links.NIPSDQNHead()
        self.pi = policy.FCSoftmaxPolicy(
            self.head.n_output_channels, n_actions)
        self.v = v_function.FCVFunction(self.head.n_output_channels)
        super().__init__(self.head, self.pi, self.v)

    def pi_and_v(self, state):
        out = self.head(state)
        return self.pi(out), self.v(out)


class A3CLSTM(chainer.ChainList, a3c.A3CModel, RecurrentChainMixin):

    def __init__(self, n_actions):
        self.head = links.NIPSDQNHead()
        self.pi = policy.FCSoftmaxPolicy(
            self.head.n_output_channels, n_actions)
        self.v = v_function.FCVFunction(self.head.n_output_channels)
        self.lstm = L.LSTM(self.head.n_output_channels,
                           self.head.n_output_channels)
        super().__init__(self.head, self.lstm, self.pi, self.v)

    def pi_and_v(self, state):
        h = self.head(state)
        h = self.lstm(h)
        return self.pi(h), self.v(h)


def main():

    parser = argparse.ArgumentParser()
    parser.add_argument('processes', type=int)
    parser.add_argument('--env', type=str, default='BreakoutNoFrameskip-v4')
    parser.add_argument('--seed', type=int, default=0,
                        help='Random seed [0, 2 ** 31)')
    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('--t-max', type=int, default=5)
    parser.add_argument('--beta', type=float, default=1e-2)
    parser.add_argument('--profile', action='store_true')
    parser.add_argument('--steps', type=int, default=8 * 10 ** 7)
    parser.add_argument('--max-frames', type=int,
                        default=30 * 60 * 60,  # 30 minutes with 60 fps
                        help='Maximum number of frames for each episode.')
    parser.add_argument('--lr', type=float, default=7e-4)
    parser.add_argument('--eval-interval', type=int, default=10 ** 6)
    parser.add_argument('--eval-n-runs', type=int, default=10)
    parser.add_argument('--weight-decay', type=float, default=0.0)
    parser.add_argument('--use-lstm', action='store_true')
    parser.add_argument('--demo', action='store_true', default=False)
    parser.add_argument('--load', type=str, default='')
    parser.add_argument('--logging-level', type=int, default=20,
                        help='Logging level. 10:DEBUG, 20:INFO etc.')
    parser.add_argument('--render', action='store_true', default=False,
                        help='Render env states in a GUI window.')
    parser.add_argument('--monitor', action='store_true', default=False,
                        help='Monitor env. Videos and additional information'
                             ' are saved as output files.')
    parser.set_defaults(use_lstm=False)
    args = parser.parse_args()

    import logging
    logging.basicConfig(level=args.logging_level)

    # Set a random seed used in ChainerRL.
    # If you use more than one process (i.e. processes > 1),
    # the results will be no longer be deterministic
    # even with the same random seed.
    misc.set_random_seed(args.seed)

    # Set different random seeds for different subprocesses.
    # If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
    # If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
    process_seeds = np.arange(args.processes) + args.seed * args.processes
    assert process_seeds.max() < 2 ** 31

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

    n_actions = gym.make(args.env).action_space.n

    if args.use_lstm:
        model = A3CLSTM(n_actions)
    else:
        model = A3CFF(n_actions)

    # Draw the computational graph and save it in the output directory.
    fake_obs = chainer.Variable(
        np.zeros((4, 84, 84), dtype=np.float32)[None],
        name='observation')
    with chainerrl.recurrent.state_reset(model):
        # The state of the model is reset again after drawing the graph
        chainerrl.misc.draw_computational_graph(
            [model(fake_obs)],
            os.path.join(args.outdir, 'model'))

    opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=1e-1, alpha=0.99)
    opt.setup(model)
    opt.add_hook(chainer.optimizer.GradientClipping(40))
    if args.weight_decay > 0:
        opt.add_hook(NonbiasWeightDecay(args.weight_decay))

    def phi(x):
        # Feature extractor
        return np.asarray(x, dtype=np.float32) / 255

    agent = a3c.A3C(model, opt, t_max=args.t_max, gamma=0.99,
                    beta=args.beta, phi=phi)

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

    def make_env(process_idx, test):
        # Use different random seeds for train and test envs
        process_seed = process_seeds[process_idx]
        env_seed = 2 ** 31 - 1 - process_seed if test else process_seed
        env = atari_wrappers.wrap_deepmind(
            atari_wrappers.make_atari(args.env, max_frames=args.max_frames),
            episode_life=not test,
            clip_rewards=not test)
        env.seed(int(env_seed))
        if args.monitor:
            env = chainerrl.wrappers.Monitor(
                env, args.outdir,
                mode='evaluation' if test else 'training')
        if args.render:
            env = chainerrl.wrappers.Render(env)
        return env

    if args.demo:
        env = make_env(0, True)
        eval_stats = experiments.eval_performance(
            env=env,
            agent=agent,
            n_steps=None,
            n_episodes=args.eval_n_runs)
        print('n_runs: {} mean: {} median: {} stdev: {}'.format(
            args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
            eval_stats['stdev']))
    else:

        # Linearly decay the learning rate to zero
        def lr_setter(env, agent, value):
            agent.optimizer.lr = value

        lr_decay_hook = experiments.LinearInterpolationHook(
            args.steps, args.lr, 0, lr_setter)

        experiments.train_agent_async(
            agent=agent,
            outdir=args.outdir,
            processes=args.processes,
            make_env=make_env,
            profile=args.profile,
            steps=args.steps,
            eval_n_steps=None,
            eval_n_episodes=args.eval_n_runs,
            eval_interval=args.eval_interval,
            global_step_hooks=[lr_decay_hook],
            save_best_so_far_agent=False,
        )


if __name__ == '__main__':
    main()