60d1ba1c8f
Needed due to a breaking change in the Collector which was overlooked in some of the examples
175 lines
6.4 KiB
Python
175 lines
6.4 KiB
Python
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 gymnasium as gym
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import numpy as np
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import torch
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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 ContinuousToDiscrete, SubprocVectorEnv
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from tianshou.policy import BranchingDQNPolicy
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from tianshou.policy.base import BasePolicy
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from tianshou.trainer import OffpolicyTrainer
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from tianshou.utils import TensorboardLogger
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from tianshou.utils.net.common import BranchingNet
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def get_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser()
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# task
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parser.add_argument("--task", type=str, default="BipedalWalker-v3")
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# network architecture
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parser.add_argument("--common-hidden-sizes", type=int, nargs="*", default=[512, 256])
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parser.add_argument("--action-hidden-sizes", type=int, nargs="*", default=[128])
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parser.add_argument("--value-hidden-sizes", type=int, nargs="*", default=[128])
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parser.add_argument("--action-per-branch", type=int, default=25)
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# training hyperparameters
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--eps-test", type=float, default=0.0)
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parser.add_argument("--eps-train", type=float, default=0.73)
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parser.add_argument("--eps-decay", type=float, default=5e-6)
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parser.add_argument("--buffer-size", type=int, default=100000)
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parser.add_argument("--lr", type=float, default=1e-4)
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parser.add_argument("--gamma", type=float, default=0.99)
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parser.add_argument("--target-update-freq", type=int, default=1000)
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parser.add_argument("--epoch", type=int, default=25)
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parser.add_argument("--step-per-epoch", type=int, default=80000)
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parser.add_argument("--step-per-collect", type=int, default=16)
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parser.add_argument("--update-per-step", type=float, default=0.0625)
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parser.add_argument("--batch-size", type=int, default=512)
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parser.add_argument("--training-num", type=int, default=20)
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parser.add_argument("--test-num", type=int, default=10)
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# other
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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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return parser.parse_args()
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def test_bdq(args: argparse.Namespace = get_args()) -> None:
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env = gym.make(args.task)
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env = ContinuousToDiscrete(env, args.action_per_branch)
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assert isinstance(env.action_space, gym.spaces.MultiDiscrete)
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assert isinstance(
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env.observation_space,
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gym.spaces.Box,
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) # BipedalWalker-v3 has `Box` observation space by design
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args.state_shape = env.observation_space.shape
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args.action_shape = env.action_space.shape
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args.num_branches = args.action_shape[0]
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print("Observations shape:", args.state_shape)
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print("Num branches:", args.num_branches)
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print("Actions per branch:", args.action_per_branch)
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# train_envs = ContinuousToDiscrete(gym.make(args.task), args.action_per_branch)
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# you can also use tianshou.env.SubprocVectorEnv
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train_envs = SubprocVectorEnv(
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[
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lambda: ContinuousToDiscrete(gym.make(args.task), args.action_per_branch)
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for _ in range(args.training_num)
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],
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)
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# test_envs = ContinuousToDiscrete(gym.make(args.task), args.action_per_branch)
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test_envs = SubprocVectorEnv(
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[
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lambda: ContinuousToDiscrete(gym.make(args.task), args.action_per_branch)
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for _ in range(args.test_num)
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],
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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 = BranchingNet(
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args.state_shape,
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args.num_branches,
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args.action_per_branch,
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args.common_hidden_sizes,
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args.value_hidden_sizes,
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args.action_hidden_sizes,
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device=args.device,
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).to(args.device)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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policy: BranchingDQNPolicy = BranchingDQNPolicy(
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model=net,
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optim=optim,
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discount_factor=args.gamma,
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action_space=env.action_space, # type: ignore[arg-type] # TODO: should `BranchingDQNPolicy` support also `MultiDiscrete` action spaces?
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target_update_freq=args.target_update_freq,
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)
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# collector
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train_collector = Collector(
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policy,
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train_envs,
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VectorReplayBuffer(args.buffer_size, len(train_envs)),
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exploration_noise=True,
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)
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test_collector = Collector(policy, test_envs, exploration_noise=False)
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# policy.set_eps(1)
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train_collector.reset()
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train_collector.collect(n_step=args.batch_size * args.training_num)
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# log
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current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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log_path = os.path.join(args.logdir, "bdq", args.task, current_time)
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writer = SummaryWriter(log_path)
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logger = TensorboardLogger(writer)
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def save_best_fn(policy: BasePolicy) -> None:
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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: float) -> bool:
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if env.spec and env.spec.reward_threshold:
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return mean_rewards >= env.spec.reward_threshold
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return False
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def train_fn(epoch: int, env_step: int) -> None: # exp decay
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eps = max(args.eps_train * (1 - args.eps_decay) ** env_step, args.eps_test)
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policy.set_eps(eps)
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def test_fn(epoch: int, env_step: int | None) -> None:
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policy.set_eps(args.eps_test)
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# trainer
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result = OffpolicyTrainer(
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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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step_per_collect=args.step_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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update_per_step=args.update_per_step,
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stop_fn=stop_fn,
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train_fn=train_fn,
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test_fn=test_fn,
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save_best_fn=save_best_fn,
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logger=logger,
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).run()
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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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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_bdq(get_args())
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