b900fdf6f2
Closes #947 This removes all kwargs from all policy constructors. While doing that, I also improved several names and added a whole lot of TODOs. ## Functional changes: 1. Added possibility to pass None as `critic2` and `critic2_optim`. In fact, the default behavior then should cover the absolute majority of cases 2. Added a function called `clone_optimizer` as a temporary measure to support passing `critic2_optim=None` ## Breaking changes: 1. `action_space` is no longer optional. In fact, it already was non-optional, as there was a ValueError in BasePolicy.init. So now several examples were fixed to reflect that 2. `reward_normalization` removed from DDPG and children. It was never allowed to pass it as `True` there, an error would have been raised in `compute_n_step_reward`. Now I removed it from the interface 3. renamed `critic1` and similar to `critic`, in order to have uniform interfaces. Note that the `critic` in DDPG was optional for the sole reason that child classes used `critic1`. I removed this optionality (DDPG can't do anything with `critic=None`) 4. Several renamings of fields (mostly private to public, so backwards compatible) ## Additional changes: 1. Removed type and default declaration from docstring. This kind of duplication is really not necessary 2. Policy constructors are now only called using named arguments, not a fragile mixture of positional and named as before 5. Minor beautifications in typing and code 6. Generally shortened docstrings and made them uniform across all policies (hopefully) ## Comment: With these changes, several problems in tianshou's inheritance hierarchy become more apparent. I tried highlighting them for future work. --------- Co-authored-by: Dominik Jain <d.jain@appliedai.de>
235 lines
8.4 KiB
Python
Executable File
235 lines
8.4 KiB
Python
Executable File
#!/usr/bin/env python3
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import argparse
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import datetime
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import os
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import pprint
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import numpy as np
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import torch
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from mujoco_env import make_mujoco_env
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from torch import nn
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from torch.distributions import Independent, Normal
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from torch.optim.lr_scheduler import LambdaLR
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import Collector, ReplayBuffer, VectorReplayBuffer
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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, WandbLogger
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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="Ant-v3")
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--buffer-size", type=int, default=4096)
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parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
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parser.add_argument("--lr", type=float, default=3e-4)
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parser.add_argument("--gamma", type=float, default=0.99)
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parser.add_argument("--epoch", type=int, default=100)
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parser.add_argument("--step-per-epoch", type=int, default=30000)
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parser.add_argument("--step-per-collect", type=int, default=2048)
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parser.add_argument("--repeat-per-collect", type=int, default=10)
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parser.add_argument("--batch-size", type=int, default=64)
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parser.add_argument("--training-num", type=int, default=64)
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parser.add_argument("--test-num", type=int, default=10)
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# ppo special
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parser.add_argument("--rew-norm", type=int, default=True)
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# In theory, `vf-coef` will not make any difference if using Adam optimizer.
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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("--gae-lambda", type=float, default=0.95)
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parser.add_argument("--bound-action-method", type=str, default="clip")
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parser.add_argument("--lr-decay", type=int, default=True)
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parser.add_argument("--max-grad-norm", type=float, default=0.5)
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parser.add_argument("--eps-clip", type=float, default=0.2)
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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=0)
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parser.add_argument("--norm-adv", type=int, default=0)
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parser.add_argument("--recompute-adv", type=int, default=1)
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parser.add_argument("--logdir", type=str, default="log")
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parser.add_argument("--render", type=float, default=0.0)
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parser.add_argument(
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"--device",
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type=str,
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default="cuda" if torch.cuda.is_available() else "cpu",
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)
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parser.add_argument("--resume-path", type=str, default=None)
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parser.add_argument("--resume-id", type=str, default=None)
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parser.add_argument(
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"--logger",
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type=str,
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default="tensorboard",
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choices=["tensorboard", "wandb"],
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)
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parser.add_argument("--wandb-project", type=str, default="mujoco.benchmark")
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parser.add_argument(
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"--watch",
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default=False,
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action="store_true",
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help="watch the play of pre-trained policy only",
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)
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return parser.parse_args()
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def test_ppo(args=get_args()):
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env, train_envs, test_envs = make_mujoco_env(
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args.task,
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args.seed,
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args.training_num,
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args.test_num,
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obs_norm=True,
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)
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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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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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print("Action range:", np.min(env.action_space.low), np.max(env.action_space.high))
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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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# model
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net_a = Net(
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args.state_shape,
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hidden_sizes=args.hidden_sizes,
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activation=nn.Tanh,
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device=args.device,
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)
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actor = ActorProb(
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net_a,
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args.action_shape,
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unbounded=True,
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device=args.device,
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).to(args.device)
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net_c = Net(
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args.state_shape,
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hidden_sizes=args.hidden_sizes,
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activation=nn.Tanh,
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device=args.device,
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)
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critic = Critic(net_c, device=args.device).to(args.device)
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actor_critic = ActorCritic(actor, critic)
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torch.nn.init.constant_(actor.sigma_param, -0.5)
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for m in actor_critic.modules():
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if isinstance(m, torch.nn.Linear):
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# orthogonal initialization
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torch.nn.init.orthogonal_(m.weight, gain=np.sqrt(2))
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torch.nn.init.zeros_(m.bias)
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# do last policy layer scaling, this will make initial actions have (close to)
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# 0 mean and std, and will help boost performances,
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# see https://arxiv.org/abs/2006.05990, Fig.24 for details
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for m in actor.mu.modules():
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if isinstance(m, torch.nn.Linear):
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torch.nn.init.zeros_(m.bias)
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m.weight.data.copy_(0.01 * m.weight.data)
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optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
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lr_scheduler = None
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if args.lr_decay:
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# decay learning rate to 0 linearly
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max_update_num = np.ceil(args.step_per_epoch / args.step_per_collect) * args.epoch
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lr_scheduler = LambdaLR(optim, lr_lambda=lambda epoch: 1 - epoch / max_update_num)
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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=actor,
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critic=critic,
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optim=optim,
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dist_fn=dist,
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discount_factor=args.gamma,
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gae_lambda=args.gae_lambda,
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max_grad_norm=args.max_grad_norm,
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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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action_scaling=True,
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action_bound_method=args.bound_action_method,
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lr_scheduler=lr_scheduler,
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action_space=env.action_space,
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eps_clip=args.eps_clip,
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value_clip=args.value_clip,
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dual_clip=args.dual_clip,
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advantage_normalization=args.norm_adv,
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recompute_advantage=args.recompute_adv,
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)
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# load a previous policy
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if args.resume_path:
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ckpt = torch.load(args.resume_path, map_location=args.device)
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policy.load_state_dict(ckpt["model"])
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train_envs.set_obs_rms(ckpt["obs_rms"])
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test_envs.set_obs_rms(ckpt["obs_rms"])
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print("Loaded agent from: ", args.resume_path)
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# collector
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if args.training_num > 1:
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buffer = VectorReplayBuffer(args.buffer_size, len(train_envs))
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else:
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buffer = ReplayBuffer(args.buffer_size)
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train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
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test_collector = Collector(policy, test_envs)
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# log
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now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
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args.algo_name = "ppo"
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log_name = os.path.join(args.task, args.algo_name, str(args.seed), now)
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log_path = os.path.join(args.logdir, log_name)
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# logger
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if args.logger == "wandb":
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logger = WandbLogger(
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save_interval=1,
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name=log_name.replace(os.path.sep, "__"),
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run_id=args.resume_id,
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config=args,
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project=args.wandb_project,
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)
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writer = SummaryWriter(log_path)
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writer.add_text("args", str(args))
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if args.logger == "tensorboard":
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logger = TensorboardLogger(writer)
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else: # wandb
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logger.load(writer)
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def save_best_fn(policy):
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state = {"model": policy.state_dict(), "obs_rms": train_envs.get_obs_rms()}
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torch.save(state, os.path.join(log_path, "policy.pth"))
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if not args.watch:
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# trainer
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result = OnpolicyTrainer(
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policy=policy,
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train_collector=train_collector,
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test_collector=test_collector,
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max_epoch=args.epoch,
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step_per_epoch=args.step_per_epoch,
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repeat_per_collect=args.repeat_per_collect,
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episode_per_test=args.test_num,
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batch_size=args.batch_size,
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step_per_collect=args.step_per_collect,
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save_best_fn=save_best_fn,
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logger=logger,
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test_in_train=False,
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).run()
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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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test_envs.seed(args.seed)
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test_collector.reset()
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result = test_collector.collect(n_episode=args.test_num, render=args.render)
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print(f'Final reward: {result["rews"].mean()}, length: {result["lens"].mean()}')
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if __name__ == "__main__":
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test_ppo()
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