94bfb32cc1
1. add policy.eval() in all test scripts' "watch performance" 2. remove dict return support for collector preprocess_fn 3. add `__contains__` and `pop` in batch: `key in batch`, `batch.pop(key, deft)` 4. exact n_episode for a list of n_episode limitation and save fake data in cache_buffer when self.buffer is None (#184) 5. fix tensorboard logging: h-axis stands for env step instead of gradient step; add test results into tensorboard 6. add test_returns (both GAE and nstep) 7. change the type-checking order in batch.py and converter.py in order to meet the most often case first 8. fix shape inconsistency for torch.Tensor in replay buffer 9. remove `**kwargs` in ReplayBuffer 10. remove default value in batch.split() and add merge_last argument (#185) 11. improve nstep efficiency 12. add max_batchsize in onpolicy algorithms 13. potential bugfix for subproc.wait 14. fix RecurrentActorProb 15. improve the code-coverage (from 90% to 95%) and remove the dead code 16. fix some incorrect type annotation The above improvement also increases the training FPS: on my computer, the previous version is only ~1800 FPS and after that, it can reach ~2050 (faster than v0.2.4.post1).
116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
import os
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import gym
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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 DummyVectorEnv
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from tianshou.policy import DQNPolicy
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from tianshou.trainer import offpolicy_trainer
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from tianshou.data import Collector, ReplayBuffer
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from tianshou.utils.net.common import Net
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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='Acrobot-v1')
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parser.add_argument('--seed', type=int, default=0)
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parser.add_argument('--eps-test', type=float, default=0.05)
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parser.add_argument('--eps-train', type=float, default=0.5)
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parser.add_argument('--buffer-size', type=int, default=20000)
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parser.add_argument('--lr', type=float, default=1e-3)
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parser.add_argument('--gamma', type=float, default=0.95)
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parser.add_argument('--n-step', type=int, default=3)
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parser.add_argument('--target-update-freq', type=int, default=320)
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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=100)
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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=0)
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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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return parser.parse_args()
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def test_dqn(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 = DummyVectorEnv(
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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 = DummyVectorEnv(
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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(args.layer_num, args.state_shape,
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args.action_shape, args.device, dueling=(2, 2)).to(args.device)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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policy = DQNPolicy(
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net, optim, args.gamma, args.n_step,
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target_update_freq=args.target_update_freq)
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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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# policy.set_eps(1)
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train_collector.collect(n_step=args.batch_size)
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# log
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log_path = os.path.join(args.logdir, args.task, 'dqn')
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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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def train_fn(x):
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if x <= int(0.1 * args.epoch):
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policy.set_eps(args.eps_train)
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elif x <= int(0.5 * args.epoch):
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eps = args.eps_train - (x - 0.1 * args.epoch) / \
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(0.4 * args.epoch) * (0.5 * args.eps_train)
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policy.set_eps(eps)
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else:
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policy.set_eps(0.5 * args.eps_train)
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def test_fn(x):
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policy.set_eps(args.eps_test)
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# trainer
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result = offpolicy_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.test_num,
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args.batch_size, train_fn=train_fn, test_fn=test_fn,
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stop_fn=stop_fn, save_fn=save_fn, writer=writer)
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assert stop_fn(result['best_reward'])
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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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policy.eval()
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policy.set_eps(args.eps_test)
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test_envs.seed(args.seed)
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test_collector.reset()
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result = test_collector.collect(n_episode=[1] * args.test_num,
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render=args.render)
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print(f'Final reward: {result["rew"]}, length: {result["len"]}')
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if __name__ == '__main__':
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test_dqn(get_args())
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