Tianshou/test/modelbased/test_dqn_icm.py

import argparse
import os
import pprint

import gymnasium as gym
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter

from tianshou.data import Collector, PrioritizedVectorReplayBuffer, VectorReplayBuffer
from tianshou.env import DummyVectorEnv
from tianshou.policy import DQNPolicy, ICMPolicy
from tianshou.trainer import offpolicy_trainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.common import MLP, Net
from tianshou.utils.net.discrete import IntrinsicCuriosityModule


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--task', type=str, default='CartPole-v0')
    parser.add_argument('--reward-threshold', type=float, default=None)
    parser.add_argument('--seed', type=int, default=1626)
    parser.add_argument('--eps-test', type=float, default=0.05)
    parser.add_argument('--eps-train', type=float, default=0.1)
    parser.add_argument('--buffer-size', type=int, default=20000)
    parser.add_argument('--lr', type=float, default=1e-3)
    parser.add_argument('--gamma', type=float, default=0.9)
    parser.add_argument('--n-step', type=int, default=3)
    parser.add_argument('--target-update-freq', type=int, default=320)
    parser.add_argument('--epoch', type=int, default=20)
    parser.add_argument('--step-per-epoch', type=int, default=10000)
    parser.add_argument('--step-per-collect', type=int, default=10)
    parser.add_argument('--update-per-step', type=float, default=0.1)
    parser.add_argument('--batch-size', type=int, default=64)
    parser.add_argument(
        '--hidden-sizes', type=int, nargs='*', default=[128, 128, 128, 128]
    )
    parser.add_argument('--training-num', type=int, default=10)
    parser.add_argument('--test-num', type=int, default=100)
    parser.add_argument('--logdir', type=str, default='log')
    parser.add_argument('--render', type=float, default=0.)
    parser.add_argument('--prioritized-replay', action="store_true", default=False)
    parser.add_argument('--alpha', type=float, default=0.6)
    parser.add_argument('--beta', type=float, default=0.4)
    parser.add_argument(
        '--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu'
    )
    parser.add_argument(
        '--lr-scale',
        type=float,
        default=1.,
        help='use intrinsic curiosity module with this lr scale'
    )
    parser.add_argument(
        '--reward-scale',
        type=float,
        default=0.01,
        help='scaling factor for intrinsic curiosity reward'
    )
    parser.add_argument(
        '--forward-loss-weight',
        type=float,
        default=0.2,
        help='weight for the forward model loss in ICM'
    )
    args = parser.parse_known_args()[0]
    return args


def test_dqn_icm(args=get_args()):
    env = gym.make(args.task)
    args.state_shape = env.observation_space.shape or env.observation_space.n
    args.action_shape = env.action_space.shape or env.action_space.n
    if args.reward_threshold is None:
        default_reward_threshold = {"CartPole-v0": 195}
        args.reward_threshold = default_reward_threshold.get(
            args.task, env.spec.reward_threshold
        )
    # train_envs = gym.make(args.task)
    # you can also use tianshou.env.SubprocVectorEnv
    train_envs = DummyVectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.training_num)]
    )
    # test_envs = gym.make(args.task)
    test_envs = DummyVectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.test_num)]
    )
    # seed
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    train_envs.seed(args.seed)
    test_envs.seed(args.seed)
    # Q_param = V_param = {"hidden_sizes": [128]}
    # model
    net = Net(
        args.state_shape,
        args.action_shape,
        hidden_sizes=args.hidden_sizes,
        device=args.device,
        # dueling=(Q_param, V_param),
    ).to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    policy = DQNPolicy(
        net,
        optim,
        args.gamma,
        args.n_step,
        target_update_freq=args.target_update_freq,
    )
    feature_dim = args.hidden_sizes[-1]
    feature_net = MLP(
        np.prod(args.state_shape),
        output_dim=feature_dim,
        hidden_sizes=args.hidden_sizes[:-1],
        device=args.device
    )
    action_dim = np.prod(args.action_shape)
    icm_net = IntrinsicCuriosityModule(
        feature_net,
        feature_dim,
        action_dim,
        hidden_sizes=args.hidden_sizes[-1:],
        device=args.device
    ).to(args.device)
    icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.lr)
    policy = ICMPolicy(
        policy, icm_net, icm_optim, args.lr_scale, args.reward_scale,
        args.forward_loss_weight
    )
    # buffer
    if args.prioritized_replay:
        buf = PrioritizedVectorReplayBuffer(
            args.buffer_size,
            buffer_num=len(train_envs),
            alpha=args.alpha,
            beta=args.beta,
        )
    else:
        buf = VectorReplayBuffer(args.buffer_size, buffer_num=len(train_envs))
    # collector
    train_collector = Collector(policy, train_envs, buf, exploration_noise=True)
    test_collector = Collector(policy, test_envs, exploration_noise=True)
    # policy.set_eps(1)
    train_collector.collect(n_step=args.batch_size * args.training_num)
    # log
    log_path = os.path.join(args.logdir, args.task, 'dqn_icm')
    writer = SummaryWriter(log_path)
    logger = TensorboardLogger(writer)

    def save_best_fn(policy):
        torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))

    def stop_fn(mean_rewards):
        return mean_rewards >= args.reward_threshold

    def train_fn(epoch, env_step):
        # eps annnealing, just a demo
        if env_step <= 10000:
            policy.set_eps(args.eps_train)
        elif env_step <= 50000:
            eps = args.eps_train - (env_step - 10000) / \
                40000 * (0.9 * args.eps_train)
            policy.set_eps(eps)
        else:
            policy.set_eps(0.1 * args.eps_train)

    def test_fn(epoch, env_step):
        policy.set_eps(args.eps_test)

    # trainer
    result = offpolicy_trainer(
        policy,
        train_collector,
        test_collector,
        args.epoch,
        args.step_per_epoch,
        args.step_per_collect,
        args.test_num,
        args.batch_size,
        update_per_step=args.update_per_step,
        train_fn=train_fn,
        test_fn=test_fn,
        stop_fn=stop_fn,
        save_best_fn=save_best_fn,
        logger=logger,
    )
    assert stop_fn(result['best_reward'])

    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        env = gym.make(args.task)
        policy.eval()
        policy.set_eps(args.eps_test)
        collector = Collector(policy, env)
        result = collector.collect(n_episode=1, render=args.render)
        rews, lens = result["rews"], result["lens"]
        print(f"Final reward: {rews.mean()}, length: {lens.mean()}")


if __name__ == '__main__':
    test_dqn_icm(get_args())