mirror of
https://github.com/vale981/ray
synced 2025-03-05 18:11:42 -05:00
e3c9f7e83a
* custom action dist wip * Test case for custom action dist * ActionDistribution.get_parameter_shape_for_action_space pattern * Edit exception message to also suggest using a custom action distribution * Clean up ModelCatalog.get_action_dist * Pass model config to ActionDistribution constructors * Update custom action distribution test case * Name fix * Autoformatter * parameter shape static methods for torch distributions * Fix docstring * Generalize fake array for graph initialization * Fix action dist constructors * Correct parameter shape static methods for multicategorical and gaussian * Make suggested changes to custom action dist's * Correct instances of not passing model config to action dist * Autoformatter * fix tuple distribution constructor * bugfix
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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import ray
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from ray import tune
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from ray.rllib.agents.trainer_template import build_trainer
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.policy.torch_policy_template import build_torch_policy
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parser = argparse.ArgumentParser()
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parser.add_argument("--iters", type=int, default=200)
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def policy_gradient_loss(policy, batch_tensors):
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logits, _ = policy.model({
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SampleBatch.CUR_OBS: batch_tensors[SampleBatch.CUR_OBS]
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})
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action_dist = policy.dist_class(logits, policy.config["model"])
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log_probs = action_dist.logp(batch_tensors[SampleBatch.ACTIONS])
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return -batch_tensors[SampleBatch.REWARDS].dot(log_probs)
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# <class 'ray.rllib.policy.torch_policy_template.MyTorchPolicy'>
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MyTorchPolicy = build_torch_policy(
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name="MyTorchPolicy", loss_fn=policy_gradient_loss)
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# <class 'ray.rllib.agents.trainer_template.MyCustomTrainer'>
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MyTrainer = build_trainer(
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name="MyCustomTrainer",
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default_policy=MyTorchPolicy,
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)
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if __name__ == "__main__":
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ray.init()
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args = parser.parse_args()
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tune.run(
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MyTrainer,
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stop={"training_iteration": args.iters},
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config={
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"env": "CartPole-v0",
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"num_workers": 2,
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})
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