mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
139 lines
4.4 KiB
Python
139 lines
4.4 KiB
Python
"""
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This script demonstrates how one can specify custom env APIs in
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combination with RLlib's `remote_worker_envs` setting, which
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parallelizes individual sub-envs within a vector env by making each
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one a ray Actor.
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You can access your Env's API via a custom callback as shown below.
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"""
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import argparse
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import gym
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import os
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import ray
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from ray.rllib.agents.callbacks import DefaultCallbacks
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from ray.rllib.env.apis.task_settable_env import TaskSettableEnv
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from ray.rllib.utils.test_utils import check_learning_achieved
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from ray import tune
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--run",
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type=str,
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default="PPO",
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help="The RLlib-registered algorithm to use.")
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parser.add_argument(
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"--framework",
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choices=["tf", "tf2", "tfe", "torch"],
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default="tf",
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help="The DL framework specifier.")
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parser.add_argument("--num-workers", type=int, default=1)
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# This should be >1, otherwise, remote envs make no sense.
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parser.add_argument("--num-envs-per-worker", type=int, default=4)
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parser.add_argument(
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"--as-test",
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action="store_true",
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help="Whether this script should be run as a test: --stop-reward must "
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"be achieved within --stop-timesteps AND --stop-iters.")
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parser.add_argument(
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"--stop-iters",
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type=int,
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default=50,
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help="Number of iterations to train.")
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parser.add_argument(
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"--stop-timesteps",
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type=int,
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default=100000,
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help="Number of timesteps to train.")
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parser.add_argument(
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"--stop-reward",
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type=float,
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default=180.0,
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help="Reward at which we stop training.")
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class NonVectorizedEnvToBeVectorizedIntoRemoteVectorEnv(TaskSettableEnv):
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"""Class for a single sub-env to be vectorized into RemoteVectorEnv.
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If you specify this class directly under the "env" config key, RLlib
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will auto-wrap
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Note that you may implement your own custom APIs. Here, we demonstrate
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using RLlib's TaskSettableEnv API (which is a simple sub-class
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of gym.Env).
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"""
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def __init__(self, config):
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self.action_space = gym.spaces.Box(0, 1, shape=(1, ))
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self.observation_space = gym.spaces.Box(0, 1, shape=(2, ))
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self.task = 1
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def reset(self):
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self.steps = 0
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return self.observation_space.sample()
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def step(self, action):
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self.steps += 1
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return self.observation_space.sample(), 0, self.steps > 10, {}
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def set_task(self, task) -> None:
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"""We can set the task of each sub-env (ray actor)"""
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print("Task set to {}".format(task))
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self.task = task
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class TaskSettingCallback(DefaultCallbacks):
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"""Custom callback to verify, we can set the task on each remote sub-env.
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"""
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def on_train_result(self, *, trainer, result: dict, **kwargs) -> None:
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""" Curriculum learning as seen in Ray docs """
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if result["episode_reward_mean"] > 0.0:
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phase = 0
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else:
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phase = 1
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# Sub-envs are now ray.actor.ActorHandles, so we have to add
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# `remote()` here.
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trainer.workers.foreach_env(lambda env: env.set_task.remote(phase))
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init(num_cpus=6)
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config = {
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# Specify your custom (single, non-vectorized) env directly as a
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# class. This way, RLlib can auto-create Actors from this class
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# and handle everything correctly.
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# TODO: Test for multi-agent case.
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"env": NonVectorizedEnvToBeVectorizedIntoRemoteVectorEnv,
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# Set up our own callbacks.
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"callbacks": TaskSettingCallback,
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# Force sub-envs to be ray.actor.ActorHandles, so we can step
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# through them in parallel.
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"remote_worker_envs": True,
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# How many RolloutWorkers (each with n environment copies:
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# `num_envs_per_worker`)?
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"num_workers": args.num_workers,
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# This setting should not really matter as it does not affect the
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# number of GPUs reserved for each worker.
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"num_envs_per_worker": args.num_envs_per_worker,
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# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
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"num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
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"framework": args.framework,
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}
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stop = {
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"training_iteration": args.stop_iters,
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"timesteps_total": args.stop_timesteps,
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"episode_reward_mean": args.stop_reward,
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}
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results = tune.run(args.run, config=config, stop=stop, verbose=1)
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if args.as_test:
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check_learning_achieved(results, args.stop_reward)
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ray.shutdown()
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