10d919052b
The new proposed feature is to have trainers as generators.
The usage pattern is:
```python
trainer = OnPolicyTrainer(...)
for epoch, epoch_stat, info in trainer:
print(f"Epoch: {epoch}")
print(epoch_stat)
print(info)
do_something_with_policy()
query_something_about_policy()
make_a_plot_with(epoch_stat)
display(info)
```
- epoch int: the epoch number
- epoch_stat dict: a large collection of metrics of the current epoch, including stat
- info dict: the usual dict out of the non-generator version of the trainer
You can even iterate on several different trainers at the same time:
```python
trainer1 = OnPolicyTrainer(...)
trainer2 = OnPolicyTrainer(...)
for result1, result2, ... in zip(trainer1, trainer2, ...):
compare_results(result1, result2, ...)
```
Co-authored-by: Jiayi Weng <trinkle23897@gmail.com>
202 lines
7.3 KiB
Python
202 lines
7.3 KiB
Python
import argparse
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import os
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import pprint
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import gym
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import numpy as np
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import torch
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from torch.distributions import Independent, Normal
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import Collector, VectorReplayBuffer
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from tianshou.env import DummyVectorEnv
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from tianshou.policy import PPOPolicy
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from tianshou.trainer import OnpolicyTrainer
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from tianshou.utils import TensorboardLogger
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from tianshou.utils.net.common import ActorCritic, Net
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from tianshou.utils.net.continuous import ActorProb, Critic
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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='Pendulum-v1')
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parser.add_argument('--reward-threshold', type=float, default=None)
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parser.add_argument('--seed', type=int, default=1)
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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('--epoch', type=int, default=5)
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parser.add_argument('--step-per-epoch', type=int, default=150000)
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parser.add_argument('--episode-per-collect', type=int, default=16)
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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=128)
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parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64])
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parser.add_argument('--training-num', type=int, default=16)
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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, default='cuda' if torch.cuda.is_available() else 'cpu'
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)
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# ppo special
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parser.add_argument('--vf-coef', type=float, default=0.25)
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parser.add_argument('--ent-coef', type=float, default=0.0)
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parser.add_argument('--eps-clip', type=float, default=0.2)
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parser.add_argument('--max-grad-norm', type=float, default=0.5)
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parser.add_argument('--gae-lambda', type=float, default=0.95)
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parser.add_argument('--rew-norm', type=int, default=1)
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parser.add_argument('--dual-clip', type=float, default=None)
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parser.add_argument('--value-clip', type=int, default=1)
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parser.add_argument('--norm-adv', type=int, default=1)
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parser.add_argument('--recompute-adv', type=int, default=0)
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parser.add_argument('--resume', action="store_true")
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parser.add_argument("--save-interval", type=int, default=4)
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args = parser.parse_known_args()[0]
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return args
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def test_ppo(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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args.max_action = env.action_space.high[0]
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if args.reward_threshold is None:
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default_reward_threshold = {"Pendulum-v0": -250, "Pendulum-v1": -250}
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args.reward_threshold = default_reward_threshold.get(
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args.task, env.spec.reward_threshold
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)
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# you can also use tianshou.env.SubprocVectorEnv
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# train_envs = gym.make(args.task)
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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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)
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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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)
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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.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
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actor = ActorProb(
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net, args.action_shape, max_action=args.max_action, device=args.device
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).to(args.device)
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critic = Critic(
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Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device),
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device=args.device
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).to(args.device)
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actor_critic = ActorCritic(actor, critic)
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# orthogonal initialization
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for m in actor_critic.modules():
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if isinstance(m, torch.nn.Linear):
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torch.nn.init.orthogonal_(m.weight)
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torch.nn.init.zeros_(m.bias)
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optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
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# replace DiagGuassian with Independent(Normal) which is equivalent
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# pass *logits to be consistent with policy.forward
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def dist(*logits):
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return Independent(Normal(*logits), 1)
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policy = PPOPolicy(
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actor,
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critic,
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optim,
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dist,
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discount_factor=args.gamma,
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max_grad_norm=args.max_grad_norm,
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eps_clip=args.eps_clip,
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vf_coef=args.vf_coef,
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ent_coef=args.ent_coef,
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reward_normalization=args.rew_norm,
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advantage_normalization=args.norm_adv,
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recompute_advantage=args.recompute_adv,
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dual_clip=args.dual_clip,
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value_clip=args.value_clip,
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gae_lambda=args.gae_lambda,
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action_space=env.action_space
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)
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# collector
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train_collector = Collector(
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policy, train_envs, VectorReplayBuffer(args.buffer_size, len(train_envs))
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)
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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, 'ppo')
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writer = SummaryWriter(log_path)
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logger = TensorboardLogger(writer, save_interval=args.save_interval)
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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(mean_rewards):
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return mean_rewards >= args.reward_threshold
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def save_checkpoint_fn(epoch, env_step, gradient_step):
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# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
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torch.save(
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{
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'model': policy.state_dict(),
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'optim': optim.state_dict(),
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}, os.path.join(log_path, 'checkpoint.pth')
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)
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if args.resume:
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# load from existing checkpoint
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print(f"Loading agent under {log_path}")
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ckpt_path = os.path.join(log_path, 'checkpoint.pth')
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if os.path.exists(ckpt_path):
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checkpoint = torch.load(ckpt_path, map_location=args.device)
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policy.load_state_dict(checkpoint['model'])
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optim.load_state_dict(checkpoint['optim'])
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print("Successfully restore policy and optim.")
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else:
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print("Fail to restore policy and optim.")
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# trainer
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trainer = OnpolicyTrainer(
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policy,
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train_collector,
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test_collector,
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args.epoch,
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args.step_per_epoch,
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args.repeat_per_collect,
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args.test_num,
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args.batch_size,
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episode_per_collect=args.episode_per_collect,
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stop_fn=stop_fn,
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save_fn=save_fn,
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logger=logger,
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resume_from_log=args.resume,
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save_checkpoint_fn=save_checkpoint_fn
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)
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for epoch, epoch_stat, info in trainer:
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print(f"Epoch: {epoch}")
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print(epoch_stat)
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print(info)
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assert stop_fn(info["best_reward"])
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if __name__ == '__main__':
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pprint.pprint(info)
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# Let's watch its performance!
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env = gym.make(args.task)
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policy.eval()
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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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rews, lens = result["rews"], result["lens"]
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print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
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def test_ppo_resume(args=get_args()):
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args.resume = True
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test_ppo(args)
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if __name__ == '__main__':
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test_ppo()
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