8a0629ded6
Closes #952 - `SamplingConfig` supports `batch_size=None`. #1077 - tests and examples are covered by `mypy`. #1077 - `NetBase` is more used, stricter typing by making it generic. #1077 - `utils.net.common.Recurrent` now receives and returns a `RecurrentStateBatch` instead of a dict. #1077 --------- Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de>
231 lines
8.1 KiB
Python
Executable File
231 lines
8.1 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 Distribution, Independent, Normal
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from torch.optim.lr_scheduler import LambdaLR
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from tianshou.data import Collector, ReplayBuffer, VectorReplayBuffer
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from tianshou.highlevel.logger import LoggerFactoryDefault
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from tianshou.policy import A2CPolicy
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from tianshou.policy.base import BasePolicy
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from tianshou.trainer import OnpolicyTrainer
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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() -> argparse.Namespace:
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parser = argparse.ArgumentParser()
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parser.add_argument("--task", type=str, default="Ant-v4")
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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=7e-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=80)
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parser.add_argument("--repeat-per-collect", type=int, default=1)
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# batch-size >> step-per-collect means calculating all data in one singe forward.
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parser.add_argument("--batch-size", type=int, default=None)
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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=10)
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# a2c special
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parser.add_argument("--rew-norm", type=int, default=True)
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parser.add_argument("--vf-coef", type=float, default=0.5)
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parser.add_argument("--ent-coef", type=float, default=0.01)
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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("--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_a2c(args: argparse.Namespace = get_args()) -> None:
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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.RMSprop(
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actor_critic.parameters(),
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lr=args.lr,
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eps=1e-5,
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alpha=0.99,
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)
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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(loc_scale: tuple[torch.Tensor, torch.Tensor]) -> Distribution:
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loc, scale = loc_scale
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return Independent(Normal(loc, scale), 1)
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policy: A2CPolicy = A2CPolicy(
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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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)
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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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buffer: VectorReplayBuffer | ReplayBuffer
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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 = "a2c"
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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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logger_factory = LoggerFactoryDefault()
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if args.logger == "wandb":
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logger_factory.logger_type = "wandb"
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logger_factory.wandb_project = args.wandb_project
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else:
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logger_factory.logger_type = "tensorboard"
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logger = logger_factory.create_logger(
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log_dir=log_path,
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experiment_name=log_name,
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run_id=args.resume_id,
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config_dict=vars(args),
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)
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def save_best_fn(policy: BasePolicy) -> None:
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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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collector_stats = test_collector.collect(n_episode=args.test_num, render=args.render)
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print(collector_stats)
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if __name__ == "__main__":
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test_a2c()
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