Tianshou/test/discrete/test_pg.py

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

from tianshou.policy import PGPolicy
from tianshou.env import SubprocVectorEnv
from tianshou.trainer import onpolicy_trainer
from tianshou.data import Batch, Collector, ReplayBuffer

if __name__ == '__main__':
    from net import Net
else:  # pytest
    from test.discrete.net import Net


def compute_return_base(batch, aa=None, bb=None, gamma=0.1):
    returns = np.zeros_like(batch.rew)
    last = 0
    for i in reversed(range(len(batch.rew))):
        returns[i] = batch.rew[i]
        if not batch.done[i]:
            returns[i] += last * gamma
        last = returns[i]
    batch.update(returns=returns)
    return batch


def test_fn(size=2560):
    policy = PGPolicy(None, None, None, discount_factor=0.1)
    fn = policy.process_fn
    # fn = compute_return_base
    batch = Batch(
        done=np.array([1, 0, 0, 1, 0, 1, 0, 1.]),
        rew=np.array([0, 1, 2, 3, 4, 5, 6, 7.]),
    )
    batch = fn(batch, None, None)
    ans = np.array([0, 1.23, 2.3, 3, 4.5, 5, 6.7, 7])
    assert abs(batch.returns - ans).sum() <= 1e-5
    batch = Batch(
        done=np.array([0, 1, 0, 1, 0, 1, 0.]),
        rew=np.array([7, 6, 1, 2, 3, 4, 5.]),
    )
    batch = fn(batch, None, None)
    ans = np.array([7.6, 6, 1.2, 2, 3.4, 4, 5])
    assert abs(batch.returns - ans).sum() <= 1e-5
    batch = Batch(
        done=np.array([0, 1, 0, 1, 0, 0, 1.]),
        rew=np.array([7, 6, 1, 2, 3, 4, 5.]),
    )
    batch = fn(batch, None, None)
    ans = np.array([7.6, 6, 1.2, 2, 3.45, 4.5, 5])
    assert abs(batch.returns - ans).sum() <= 1e-5
    if __name__ == '__main__':
        batch = Batch(
            done=np.random.randint(100, size=size) == 0,
            rew=np.random.random(size),
        )
        cnt = 3000
        t = time.time()
        for _ in range(cnt):
            compute_return_base(batch)
        print(f'vanilla: {(time.time() - t) / cnt}')
        t = time.time()
        for _ in range(cnt):
            policy.process_fn(batch, None, None)
        print(f'policy: {(time.time() - t) / cnt}')


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--task', type=str, default='CartPole-v0')
    parser.add_argument('--seed', type=int, default=1626)
    parser.add_argument('--buffer-size', type=int, default=20000)
    parser.add_argument('--lr', type=float, default=3e-4)
    parser.add_argument('--gamma', type=float, default=0.9)
    parser.add_argument('--epoch', type=int, default=100)
    parser.add_argument('--step-per-epoch', type=int, default=1000)
    parser.add_argument('--collect-per-step', type=int, default=10)
    parser.add_argument('--repeat-per-collect', type=int, default=2)
    parser.add_argument('--batch-size', type=int, default=64)
    parser.add_argument('--layer-num', type=int, default=3)
    parser.add_argument('--training-num', type=int, default=8)
    parser.add_argument('--test-num', type=int, default=100)
    parser.add_argument('--logdir', type=str, default='log')
    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_pg(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
    # train_envs = gym.make(args.task)
    train_envs = SubprocVectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.training_num)],
        reset_after_done=True)
    # test_envs = gym.make(args.task)
    test_envs = SubprocVectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.test_num)],
        reset_after_done=False)
    # 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.layer_num, args.state_shape, args.action_shape,
        device=args.device)
    net = net.to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    dist = torch.distributions.Categorical
    policy = PGPolicy(net, optim, dist, args.gamma)
    # collector
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(args.buffer_size))
    test_collector = Collector(policy, test_envs)
    # log
    writer = SummaryWriter(args.logdir)

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

    # trainer
    result = onpolicy_trainer(
        policy, train_collector, test_collector, args.epoch,
        args.step_per_epoch, args.collect_per_step, args.repeat_per_collect,
        args.test_num, args.batch_size, stop_fn=stop_fn, writer=writer)
    assert stop_fn(result['best_reward'])
    train_collector.close()
    test_collector.close()
    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        env = gym.make(args.task)
        collector = Collector(policy, env)
        result = collector.collect(n_episode=1, render=1 / 35)
        print(f'Final reward: {result["rew"]}, length: {result["len"]}')
        collector.close()


if __name__ == '__main__':
    # test_fn()
    test_pg()