160 lines
5.4 KiB
Python
160 lines
5.4 KiB
Python
import os
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import gym
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import time
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import torch
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import pprint
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import argparse
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import numpy as np
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.env import VectorEnv
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from tianshou.policy import PGPolicy
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from tianshou.trainer import onpolicy_trainer
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from tianshou.data import Batch, Collector, ReplayBuffer
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if __name__ == '__main__':
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from net import Net
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else: # pytest
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from test.discrete.net import Net
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def compute_return_base(batch, aa=None, bb=None, gamma=0.1):
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returns = np.zeros_like(batch.rew)
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last = 0
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for i in reversed(range(len(batch.rew))):
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returns[i] = batch.rew[i]
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if not batch.done[i]:
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returns[i] += last * gamma
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last = returns[i]
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batch.returns = returns
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return batch
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def test_fn(size=2560):
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policy = PGPolicy(None, None, None, discount_factor=0.1)
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buf = ReplayBuffer(100)
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buf.add(1, 1, 1, 1, 1)
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fn = policy.process_fn
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# fn = compute_return_base
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batch = Batch(
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done=np.array([1, 0, 0, 1, 0, 1, 0, 1.]),
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rew=np.array([0, 1, 2, 3, 4, 5, 6, 7.]),
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)
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batch = fn(batch, buf, 0)
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ans = np.array([0, 1.23, 2.3, 3, 4.5, 5, 6.7, 7])
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assert abs(batch.returns - ans).sum() <= 1e-5
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batch = Batch(
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done=np.array([0, 1, 0, 1, 0, 1, 0.]),
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rew=np.array([7, 6, 1, 2, 3, 4, 5.]),
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)
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batch = fn(batch, buf, 0)
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ans = np.array([7.6, 6, 1.2, 2, 3.4, 4, 5])
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assert abs(batch.returns - ans).sum() <= 1e-5
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batch = Batch(
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done=np.array([0, 1, 0, 1, 0, 0, 1.]),
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rew=np.array([7, 6, 1, 2, 3, 4, 5.]),
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)
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batch = fn(batch, buf, 0)
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ans = np.array([7.6, 6, 1.2, 2, 3.45, 4.5, 5])
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assert abs(batch.returns - ans).sum() <= 1e-5
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if __name__ == '__main__':
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batch = Batch(
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done=np.random.randint(100, size=size) == 0,
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rew=np.random.random(size),
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)
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cnt = 3000
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t = time.time()
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for _ in range(cnt):
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compute_return_base(batch)
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print(f'vanilla: {(time.time() - t) / cnt}')
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t = time.time()
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for _ in range(cnt):
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policy.process_fn(batch, buf, 0)
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print(f'policy: {(time.time() - t) / cnt}')
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--task', type=str, default='CartPole-v0')
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parser.add_argument('--seed', type=int, default=1626)
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parser.add_argument('--buffer-size', type=int, default=20000)
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parser.add_argument('--lr', type=float, default=3e-4)
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parser.add_argument('--gamma', type=float, default=0.9)
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parser.add_argument('--epoch', type=int, default=10)
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parser.add_argument('--step-per-epoch', type=int, default=1000)
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parser.add_argument('--collect-per-step', type=int, default=10)
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parser.add_argument('--repeat-per-collect', type=int, default=2)
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parser.add_argument('--batch-size', type=int, default=64)
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parser.add_argument('--layer-num', type=int, default=3)
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parser.add_argument('--training-num', type=int, default=8)
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parser.add_argument('--test-num', type=int, default=100)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument('--render', type=float, default=0.)
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parser.add_argument(
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'--device', type=str,
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default='cuda' if torch.cuda.is_available() else 'cpu')
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args = parser.parse_known_args()[0]
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return args
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def test_pg(args=get_args()):
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env = gym.make(args.task)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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# train_envs = gym.make(args.task)
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# you can also use tianshou.env.SubprocVectorEnv
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train_envs = VectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.training_num)])
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# test_envs = gym.make(args.task)
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test_envs = VectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.test_num)])
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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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train_envs.seed(args.seed)
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test_envs.seed(args.seed)
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# model
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net = Net(
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args.layer_num, args.state_shape, args.action_shape,
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device=args.device, softmax=True)
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net = net.to(args.device)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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dist = torch.distributions.Categorical
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policy = PGPolicy(net, optim, dist, args.gamma)
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# collector
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train_collector = Collector(
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policy, train_envs, ReplayBuffer(args.buffer_size))
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test_collector = Collector(policy, test_envs)
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# log
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log_path = os.path.join(args.logdir, args.task, 'pg')
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writer = SummaryWriter(log_path)
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def save_fn(policy):
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torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
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def stop_fn(x):
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return x >= env.spec.reward_threshold
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# trainer
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result = onpolicy_trainer(
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policy, train_collector, test_collector, args.epoch,
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args.step_per_epoch, args.collect_per_step, args.repeat_per_collect,
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args.test_num, args.batch_size, stop_fn=stop_fn, save_fn=save_fn,
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writer=writer)
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assert stop_fn(result['best_reward'])
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train_collector.close()
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test_collector.close()
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if __name__ == '__main__':
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pprint.pprint(result)
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# Let's watch its performance!
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env = gym.make(args.task)
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collector = Collector(policy, env)
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result = collector.collect(n_episode=1, render=args.render)
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print(f'Final reward: {result["rew"]}, length: {result["len"]}')
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collector.close()
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if __name__ == '__main__':
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test_fn()
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test_pg()
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