evaluate.py

import argparse
import random

import gym
import torch
import torch.nn as nn

import config
from utils import preprocess

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

parser = argparse.ArgumentParser()
parser.add_argument('--env', choices=['CartPole-v0'])
parser.add_argument('--path', type=str, help='Path to stored DQN model.')
parser.add_argument('--n_eval_episodes', type=int, default=1,
                    help='Number of evaluation episodes.', nargs='?')
parser.add_argument('--render', dest='render',
                    action='store_true', help='Render the environment.')
parser.add_argument('--save_video', dest='save_video',
                    action='store_true', help='Save the episodes as video.')
parser.set_defaults(render=False)
parser.set_defaults(save_video=False)

# Hyperparameter configurations for different environments. See config.py.
ENV_CONFIGS = {
    'CartPole-v0': config.CartPole,
}


def evaluate_policy(dqn, env, env_config, args, n_episodes, render=False, verbose=False):
    """Runs {n_episodes} episodes to evaluate current policy."""
    total_return = 0

    step_number = 0
    for i in range(n_episodes):
        obs = preprocess(env.reset(), env=args.env)

        done = False
        episode_return = 0

        while not done:
            if render:
                env.render()

            action = dqn.act(obs, step_number, exploit=True).item()
            step_number = 0

            obs, reward, done, info = env.step(action)
            obs = preprocess(obs, env=args.env)

            episode_return += reward

        total_return += episode_return

        if verbose:
            print(
                f'Finished episode {i+1} with a total return of {episode_return}')

    return total_return / n_episodes


if __name__ == '__main__':
    args = parser.parse_args()

    # Initialize environment and config
    env = gym.make(args.env)
    env_config = ENV_CONFIGS[args.env]

    if args.save_video:
        env = gym.wrappers.Monitor(
            env, './video/', video_callable=lambda episode_id: True, force=True)

    # Load model from provided path.
    dqn = torch.load(args.path, map_location=device)
    dqn.eval()

    mean_return = evaluate_policy(dqn, env, env_config, args, args.n_eval_episodes,
                                  render=args.render and not args.save_video, verbose=True)
    print(
        f'The policy got a mean return of {mean_return} over {args.n_eval_episodes} episodes.')

    env.close()