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https://github.com/vale981/ray
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90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
"""Example of using RLlib's debug callbacks.
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Here we use callbacks to track the average CartPole pole angle magnitude as a
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custom metric.
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"""
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from typing import Dict
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import argparse
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import numpy as np
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import ray
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from ray import tune
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from ray.rllib.env import BaseEnv
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from ray.rllib.policy import Policy
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.evaluation import MultiAgentEpisode, RolloutWorker
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from ray.rllib.agents.callbacks import DefaultCallbacks
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class MyCallbacks(DefaultCallbacks):
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def on_episode_start(self, worker: RolloutWorker, base_env: BaseEnv,
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policies: Dict[str, Policy],
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episode: MultiAgentEpisode, **kwargs):
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print("episode {} started".format(episode.episode_id))
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episode.user_data["pole_angles"] = []
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episode.hist_data["pole_angles"] = []
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def on_episode_step(self, worker: RolloutWorker, base_env: BaseEnv,
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episode: MultiAgentEpisode, **kwargs):
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pole_angle = abs(episode.last_observation_for()[2])
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raw_angle = abs(episode.last_raw_obs_for()[2])
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assert pole_angle == raw_angle
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episode.user_data["pole_angles"].append(pole_angle)
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def on_episode_end(self, worker: RolloutWorker, base_env: BaseEnv,
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policies: Dict[str, Policy], episode: MultiAgentEpisode,
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**kwargs):
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pole_angle = np.mean(episode.user_data["pole_angles"])
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print("episode {} ended with length {} and pole angles {}".format(
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episode.episode_id, episode.length, pole_angle))
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episode.custom_metrics["pole_angle"] = pole_angle
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episode.hist_data["pole_angles"] = episode.user_data["pole_angles"]
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def on_sample_end(self, worker: RolloutWorker, samples: SampleBatch,
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**kwargs):
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print("returned sample batch of size {}".format(samples.count))
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def on_train_result(self, trainer, result: dict, **kwargs):
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print("trainer.train() result: {} -> {} episodes".format(
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trainer, result["episodes_this_iter"]))
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# you can mutate the result dict to add new fields to return
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result["callback_ok"] = True
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def on_postprocess_trajectory(
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self, worker: RolloutWorker, episode: MultiAgentEpisode,
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agent_id: str, policy_id: str, policies: Dict[str, Policy],
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postprocessed_batch: SampleBatch,
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original_batches: Dict[str, SampleBatch], **kwargs):
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print("postprocessed {} steps".format(postprocessed_batch.count))
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if "num_batches" not in episode.custom_metrics:
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episode.custom_metrics["num_batches"] = 0
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episode.custom_metrics["num_batches"] += 1
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--stop-iters", type=int, default=2000)
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args = parser.parse_args()
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ray.init()
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trials = tune.run(
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"PG",
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stop={
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"training_iteration": args.stop_iters,
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},
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config={
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"env": "CartPole-v0",
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"callbacks": MyCallbacks,
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"framework": "tf",
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},
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return_trials=True)
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# verify custom metrics for integration tests
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custom_metrics = trials[0].last_result["custom_metrics"]
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print(custom_metrics)
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assert "pole_angle_mean" in custom_metrics
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assert "pole_angle_min" in custom_metrics
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assert "pole_angle_max" in custom_metrics
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assert "num_batches_mean" in custom_metrics
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assert "callback_ok" in trials[0].last_result
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