mirror of
https://github.com/vale981/ray
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95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
from abc import ABC
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import ray
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import numpy as np
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from ray.rllib import Policy
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from ray.rllib.agents import with_common_config
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from ray.rllib.agents.trainer_template import build_trainer
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from ray.rllib.evaluation.worker_set import WorkerSet
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from ray.rllib.execution.metric_ops import StandardMetricsReporting
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from ray.rllib.execution.rollout_ops import ParallelRollouts, SelectExperiences
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from ray.rllib.examples.env.parametric_actions_cartpole import \
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ParametricActionsCartPole
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from ray.rllib.models.modelv2 import restore_original_dimensions
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from ray.rllib.utils import override
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from ray.rllib.utils.typing import TrainerConfigDict
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from ray.util.iter import LocalIterator
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from ray.tune.registry import register_env
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DEFAULT_CONFIG = with_common_config({})
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class RandomParametriclPolicy(Policy, ABC):
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"""
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Just pick a random legal action
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The outputted state of the environment needs to be a dictionary with an
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'action_mask' key containing the legal actions for the agent.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.exploration = self._create_exploration()
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@override(Policy)
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def compute_actions(self,
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obs_batch,
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state_batches=None,
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prev_action_batch=None,
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prev_reward_batch=None,
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info_batch=None,
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episodes=None,
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**kwargs):
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obs_batch = restore_original_dimensions(
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np.array(obs_batch, dtype=np.float32),
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self.observation_space,
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tensorlib=np)
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def pick_legal_action(legal_action):
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return np.random.choice(
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len(legal_action), 1, p=(legal_action / legal_action.sum()))[0]
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return [pick_legal_action(x) for x in obs_batch["action_mask"]], [], {}
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def learn_on_batch(self, samples):
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pass
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def get_weights(self):
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pass
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def set_weights(self, weights):
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pass
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def execution_plan(workers: WorkerSet,
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config: TrainerConfigDict) -> LocalIterator[dict]:
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rollouts = ParallelRollouts(workers, mode="async")
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# Collect batches for the trainable policies.
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rollouts = rollouts.for_each(
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SelectExperiences(workers.trainable_policies()))
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# Return training metrics.
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return StandardMetricsReporting(rollouts, workers, config)
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RandomParametricTrainer = build_trainer(
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name="RandomParametric",
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default_config=DEFAULT_CONFIG,
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default_policy=RandomParametriclPolicy,
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execution_plan=execution_plan)
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def main():
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register_env("pa_cartpole", lambda _: ParametricActionsCartPole(10))
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trainer = RandomParametricTrainer(env="pa_cartpole")
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result = trainer.train()
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assert result["episode_reward_mean"] > 10, result
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print("Test: OK")
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if __name__ == "__main__":
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ray.init()
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main()
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