Tianshou/test/continuous/test_sac_with_il.py

import os
import gym
import torch
import pprint
import argparse
import numpy as np
from torch.utils.tensorboard import SummaryWriter

from tianshou.utils import TensorboardLogger
from tianshou.env import DummyVectorEnv
from tianshou.utils.net.common import Net
from tianshou.trainer import offpolicy_trainer
from tianshou.data import Collector, VectorReplayBuffer
from tianshou.policy import SACPolicy, ImitationPolicy
from tianshou.utils.net.continuous import Actor, ActorProb, Critic


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--task', type=str, default='Pendulum-v0')
    parser.add_argument('--seed', type=int, default=0)
    parser.add_argument('--buffer-size', type=int, default=20000)
    parser.add_argument('--actor-lr', type=float, default=1e-3)
    parser.add_argument('--critic-lr', type=float, default=1e-3)
    parser.add_argument('--il-lr', type=float, default=1e-3)
    parser.add_argument('--gamma', type=float, default=0.99)
    parser.add_argument('--tau', type=float, default=0.005)
    parser.add_argument('--alpha', type=float, default=0.2)
    parser.add_argument('--auto-alpha', type=int, default=1)
    parser.add_argument('--alpha-lr', type=float, default=3e-4)
    parser.add_argument('--epoch', type=int, default=5)
    parser.add_argument('--step-per-epoch', type=int, default=24000)
    parser.add_argument('--il-step-per-epoch', type=int, default=500)
    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=128)
    parser.add_argument('--hidden-sizes', type=int,
                        nargs='*', default=[128, 128])
    parser.add_argument('--imitation-hidden-sizes', type=int,
                        nargs='*', default=[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('--rew-norm', action="store_true", default=False)
    parser.add_argument('--n-step', type=int, default=3)
    parser.add_argument(
        '--device', type=str,
        default='cuda' if torch.cuda.is_available() else 'cpu')
    args = parser.parse_known_args()[0]
    return args


def test_sac_with_il(args=get_args()):
    torch.set_num_threads(1)  # we just need only one thread for NN
    env = gym.make(args.task)
    if args.task == 'Pendulum-v0':
        env.spec.reward_threshold = -250
    args.state_shape = env.observation_space.shape or env.observation_space.n
    args.action_shape = env.action_space.shape or env.action_space.n
    args.max_action = env.action_space.high[0]
    # you can also use tianshou.env.SubprocVectorEnv
    # train_envs = gym.make(args.task)
    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)
    # model
    net = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
              device=args.device)
    actor = ActorProb(net, args.action_shape, max_action=args.max_action,
                      device=args.device, unbounded=True).to(args.device)
    actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
    net_c1 = Net(args.state_shape, args.action_shape,
                 hidden_sizes=args.hidden_sizes,
                 concat=True, device=args.device)
    critic1 = Critic(net_c1, device=args.device).to(args.device)
    critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
    net_c2 = Net(args.state_shape, args.action_shape,
                 hidden_sizes=args.hidden_sizes,
                 concat=True, device=args.device)
    critic2 = Critic(net_c2, device=args.device).to(args.device)
    critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)

    if args.auto_alpha:
        target_entropy = -np.prod(env.action_space.shape)
        log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
        alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
        args.alpha = (target_entropy, log_alpha, alpha_optim)

    policy = SACPolicy(
        actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
        tau=args.tau, gamma=args.gamma, alpha=args.alpha,
        reward_normalization=args.rew_norm,
        estimation_step=args.n_step, action_space=env.action_space)
    # collector
    train_collector = Collector(
        policy, train_envs,
        VectorReplayBuffer(args.buffer_size, len(train_envs)),
        exploration_noise=True)
    test_collector = Collector(policy, test_envs)
    # train_collector.collect(n_step=args.buffer_size)
    # log
    log_path = os.path.join(args.logdir, args.task, 'sac')
    writer = SummaryWriter(log_path)
    logger = TensorboardLogger(writer)

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

    def stop_fn(mean_rewards):
        return mean_rewards >= env.spec.reward_threshold

    # 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, stop_fn=stop_fn,
        save_fn=save_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()
        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()}")

    # here we define an imitation collector with a trivial policy
    policy.eval()
    if args.task == 'Pendulum-v0':
        env.spec.reward_threshold = -300  # lower the goal
    net = Actor(
        Net(args.state_shape, hidden_sizes=args.imitation_hidden_sizes,
            device=args.device),
        args.action_shape, max_action=args.max_action, device=args.device
    ).to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.il_lr)
    il_policy = ImitationPolicy(
        net, optim, action_space=env.action_space,
        action_scaling=True, action_bound_method="clip")
    il_test_collector = Collector(
        il_policy,
        DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.test_num)])
    )
    train_collector.reset()
    result = offpolicy_trainer(
        il_policy, train_collector, il_test_collector, args.epoch,
        args.il_step_per_epoch, args.step_per_collect, args.test_num,
        args.batch_size, stop_fn=stop_fn, save_fn=save_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)
        il_policy.eval()
        collector = Collector(il_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_sac_with_il()