mirror of
https://github.com/vale981/ray
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65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
import logging
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import ray
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from ray.rllib.policy.torch_policy_template import build_torch_policy
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from ray.rllib.agents.ppo.ppo_tf_policy import postprocess_ppo_gae, \
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setup_config
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from ray.rllib.agents.ppo.ppo_torch_policy import vf_preds_fetches
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from ray.rllib.agents.a3c.a3c_torch_policy import apply_grad_clipping
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from ray.rllib.utils.framework import try_import_torch
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from ray.rllib.models.catalog import ModelCatalog
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from ray.rllib.agents.maml.maml_torch_policy import setup_mixins, \
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maml_loss, maml_stats, maml_optimizer_fn, KLCoeffMixin
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torch, nn = try_import_torch()
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logger = logging.getLogger(__name__)
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def make_model_and_action_dist(policy, obs_space, action_space, config):
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# Get the output distribution class for predicting rewards and next-obs.
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policy.distr_cls_next_obs, num_outputs = ModelCatalog.get_action_dist(
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obs_space, config, dist_type="deterministic", framework="torch")
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# Build one dynamics model if we are a Worker.
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# If we are the main MAML learner, build n (num_workers) dynamics Models
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# for being able to create checkpoints for the current state of training.
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device = (torch.device("cuda")
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if torch.cuda.is_available() else torch.device("cpu"))
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policy.dynamics_model = ModelCatalog.get_model_v2(
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obs_space,
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action_space,
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num_outputs=num_outputs,
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model_config=config["dynamics_model"],
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framework="torch",
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name="dynamics_ensemble",
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).to(device)
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action_dist, num_outputs = ModelCatalog.get_action_dist(
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action_space, config, framework="torch")
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# Create the pi-model and register it with the Policy.
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policy.pi = ModelCatalog.get_model_v2(
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obs_space,
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action_space,
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num_outputs=num_outputs,
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model_config=config["model"],
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framework="torch",
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name="policy_model",
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)
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return policy.pi, action_dist
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MBMPOTorchPolicy = build_torch_policy(
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name="MBMPOTorchPolicy",
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get_default_config=lambda: ray.rllib.agents.mbmpo.mbmpo.DEFAULT_CONFIG,
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make_model_and_action_dist=make_model_and_action_dist,
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loss_fn=maml_loss,
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stats_fn=maml_stats,
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optimizer_fn=maml_optimizer_fn,
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extra_action_out_fn=vf_preds_fetches,
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postprocess_fn=postprocess_ppo_gae,
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extra_grad_process_fn=apply_grad_clipping,
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before_init=setup_config,
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after_init=setup_mixins,
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mixins=[KLCoeffMixin])
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