github源码

· 自定义用户gymnasium环境
· 使用tune搜索不同的learning rate"""
Example of a custom gym environment. Run this example for a demo.This example shows the usage of:- a custom environment- Ray Tune for grid search to try different learning ratesYou can visualize experiment results in ~/ray_results using TensorBoard.Run example with defaults:
$ python custom_env.py
For CLI options:
$ python custom_env.py --help
"""
import argparse
import gymnasium as gym
from gymnasium.spaces import Discrete, Box
import numpy as np
import os
import randomimport ray
from ray import air, tune
from ray.rllib.env.env_context import EnvContext
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.test_utils import check_learning_achieved
from ray.tune.logger import pretty_print
from ray.tune.registry import get_trainable_clstf1, tf, tfv = try_import_tf()
torch, nn = try_import_torch()parser = argparse.ArgumentParser()
parser.add_argument("--run", type=str, default="PPO", help="The RLlib-registered algorithm to use."
)
parser.add_argument("--framework",choices=["tf", "tf2", "torch"],default="torch",help="The DL framework specifier.",
)
parser.add_argument("--as-test",action="store_true",help="Whether this script should be run as a test: --stop-reward must ""be achieved within --stop-timesteps AND --stop-iters.",
)
parser.add_argument("--stop-iters", type=int, default=50, help="Number of iterations to train."
)
parser.add_argument("--stop-timesteps", type=int, default=100000, help="Number of timesteps to train."
)
parser.add_argument("--stop-reward", type=float, default=0.1, help="Reward at which we stop training."
)
parser.add_argument("--no-tune",action="store_true",help="Run without Tune using a manual train loop instead. In this case,""use PPO without grid search and no TensorBoard.",
)
parser.add_argument("--local-mode",action="store_true",help="Init Ray in local mode for easier debugging.",
)class SimpleCorridor(gym.Env):"""Example of a custom env in which you have to walk down a corridor.You can configure the length of the corridor via the env config."""def __init__(self, config: EnvContext):self.end_pos = config["corridor_length"]self.cur_pos = 0self.action_space = Discrete(2)self.observation_space = Box(0.0, self.end_pos, shape=(1,), dtype=np.float32)# Set the seed. This is only used for the final (reach goal) reward.self.reset(seed=config.worker_index * config.num_workers)def reset(self, *, seed=None, options=None):random.seed(seed)self.cur_pos = 0return [self.cur_pos], {}def step(self, action):assert action in [0, 1], actionif action == 0 and self.cur_pos > 0:self.cur_pos -= 1elif action == 1:self.cur_pos += 1done = truncated = self.cur_pos >= self.end_pos# Produce a random reward when we reach the goal.return ([self.cur_pos],random.random() * 2 if done else -0.1,done,truncated,{},)if __name__ == "__main__":args = parser.parse_args()print(f"Running with following CLI options: {args}")ray.init(local_mode=args.local_mode)# Can also register the env creator function explicitly with:# register_env("corridor", lambda config: SimpleCorridor(config))config = (get_trainable_cls(args.run).get_default_config()# or "corridor" if registered above.environment(SimpleCorridor, env_config={"corridor_length": 5}).framework(args.framework).rollouts(num_rollout_workers=1)# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0..resources(num_gpus=int(os.environ.get("RLLIB_NUM_GPUS", "0"))))stop = {"training_iteration": args.stop_iters,"timesteps_total": args.stop_timesteps,"episode_reward_mean": args.stop_reward,}if args.no_tune:# manual training with train loop using PPO and fixed learning rateif args.run != "PPO":raise ValueError("Only support --run PPO with --no-tune.")print("Running manual train loop without Ray Tune.")# use fixed learning rate instead of grid search (needs tune)config.lr = 1e-3algo = config.build()# run manual training loop and print results after each iterationfor _ in range(args.stop_iters):result = algo.train()print(pretty_print(result))# stop training of the target train steps or reward are reachedif (result["timesteps_total"] >= args.stop_timestepsor result["episode_reward_mean"] >= args.stop_reward):breakalgo.stop()else:# automated run with Tune and grid search and TensorBoardprint("Training automatically with Ray Tune")tuner = tune.Tuner(args.run,param_space=config.to_dict(),run_config=air.RunConfig(stop=stop),)results = tuner.fit()if args.as_test:print("Checking if learning goals were achieved")check_learning_achieved(results, args.stop_reward)ray.shutdown()