train_slimevolley.py

# Copyright 2022 The EvoJAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Train an agent to solve the SlimeVolley task.

Slime Volleyball is a game created in the early 2000s by unknown author.

The game is very simple: the agent's goal is to get the ball to land on
the ground of its opponent's side, causing its opponent to lose a life.

Each agent starts off with five lives. The episode ends when either agent
loses all five lives, or after 3000 timesteps has passed. An agent receives
a reward of +1 when its opponent loses or -1 when it loses a life.

An agent loses when it loses 5 times in the Test environment, or if it
loses based on score count after 3000 time steps.

During Training, the game is simply played for 3000 time steps, not
terminating even when one player loses 5 times.

This task is based on:
https://otoro.net/slimevolley/
https://github.com/hardmaru/slimevolleygym

Example command to run this script: `python train_slimevolley.py --gpu-id=0`
"""

import argparse
import os
import shutil
import jax

from evojax.task.slimevolley import SlimeVolley
from evojax.policy.mlp import MLPPolicy
from evojax.algo import CMA
from evojax import Trainer
from evojax import util


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--pop-size', type=int, default=128, help='ES population size.')
    parser.add_argument(
        '--hidden-size', type=int, default=20, help='Policy hidden size.')
    parser.add_argument(
        '--num-tests', type=int, default=100, help='Number of test rollouts.')
    parser.add_argument(
        '--n-repeats', type=int, default=16, help='Training repetitions.')
    parser.add_argument(
        '--max-iter', type=int, default=500, help='Max training iterations.')
    parser.add_argument(
        '--test-interval', type=int, default=50, help='Test interval.')
    parser.add_argument(
        '--log-interval', type=int, default=10, help='Logging interval.')
    parser.add_argument(
        '--seed', type=int, default=123, help='Random seed for training.')
    parser.add_argument(
        '--init-std', type=float, default=0.5, help='Initial std.')
    parser.add_argument(
        '--gpu-id', type=str, help='GPU(s) to use.')
    parser.add_argument(
        '--debug', action='store_true', help='Debug mode.')
    config, _ = parser.parse_known_args()
    return config


def main(config):
    log_dir = './log/slimevolley'
    if not os.path.exists(log_dir):
        os.makedirs(log_dir, exist_ok=True)
    logger = util.create_logger(
        name='SlimeVolley', log_dir=log_dir, debug=config.debug)
    logger.info('EvoJAX SlimeVolley')
    logger.info('=' * 30)

    max_steps = 3000
    train_task = SlimeVolley(test=False, max_steps=max_steps)
    test_task = SlimeVolley(test=True, max_steps=max_steps)
    policy = MLPPolicy(
        input_dim=train_task.obs_shape[0],
        hidden_dims=[config.hidden_size, ],
        output_dim=train_task.act_shape[0],
        output_act_fn='tanh',
    )
    solver = CMA(
        pop_size=config.pop_size,
        param_size=policy.num_params,
        init_stdev=config.init_std,
        seed=config.seed,
        logger=logger,
    )
    # Train.
    trainer = Trainer(
        policy=policy,
        solver=solver,
        train_task=train_task,
        test_task=test_task,
        max_iter=config.max_iter,
        log_interval=config.log_interval,
        test_interval=config.test_interval,
        n_repeats=config.n_repeats,
        n_evaluations=config.num_tests,
        seed=config.seed,
        log_dir=log_dir,
        logger=logger,
    )
    trainer.run(demo_mode=False)

    # Test the final model.
    src_file = os.path.join(log_dir, 'best.npz')
    tar_file = os.path.join(log_dir, 'model.npz')
    shutil.copy(src_file, tar_file)
    trainer.model_dir = log_dir
    trainer.run(demo_mode=True)

    # Visualize the policy.
    task_reset_fn = jax.jit(test_task.reset)
    policy_reset_fn = jax.jit(policy.reset)
    step_fn = jax.jit(test_task.step)
    action_fn = jax.jit(policy.get_actions)
    best_params = trainer.solver.best_params[None, :]
    key = jax.random.PRNGKey(0)[None, :]

    task_state = task_reset_fn(key)
    policy_state = policy_reset_fn(task_state)
    screens = []
    for _ in range(max_steps):
        action, policy_state = action_fn(task_state, best_params, policy_state)
        task_state, reward, done = step_fn(task_state, action)
        screens.append(SlimeVolley.render(task_state))

    gif_file = os.path.join(log_dir, 'slimevolley.gif')
    screens[0].save(gif_file, save_all=True, append_images=screens[1:],
                    duration=40, loop=0)
    logger.info('GIF saved to {}.'.format(gif_file))


if __name__ == '__main__':
    configs = parse_args()
    if configs.gpu_id is not None:
        os.environ['CUDA_VISIBLE_DEVICES'] = configs.gpu_id
    main(configs)