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…+ refactor adv estimator registry to allow registration outside ray workers (#126) # Overview - Adds support for registering custom policy loss functions, similar to NovaSky-AI#115, - Refactors the policy loss to be a function in `ppo_utils.py` instead of a (`nn.Module` in `worker.py`) - Introduces a breaking change in renaming `trainer.algorithm.ppo_loss_type` to `trainer.algorithm.policy_loss_type` - Addresses Issue NovaSky-AI#116 by creating a new `BaseFunctionRegistry` class that uses a [named actor](https://docs.ray.io/en/latest/ray-core/actors/named-actors.html) to support the following pattern: ```python # Example of custom policy loss: "simple_baseline" def compute_simple_baseline_policy_loss( log_probs: torch.Tensor, ... ): return torch.randn(1, device=log_probs.device), 0.0 # Register the custom policy loss - outside of the ray worker PolicyLossRegistry.register("simple_baseline", compute_simple_baseline_policy_loss) @ray.remote(num_cpus=1) def skyrl_entrypoint(cfg: DictConfig): exp = BasePPOExp(cfg) exp.run() @hydra.main(config_path=config_dir, config_name="ppo_base_config", version_base=None) def main(cfg: DictConfig) -> None: # validate the arguments validate_cfg(cfg) initialize_ray(cfg) ray.get(skyrl_entrypoint.remote(cfg)) ``` this change was necessary for `PolicyLossRegistry` to be accessible, since the worker `actor_loss_fn` attribute is set in `init_model` within the `worker` actor, which is a ray actor created from within the skyrl_entrypoint ray task (and registering within the entrypoint wouldn't propagate down another layer). - updates AdvantageEstimatorRegistry to extend the same `BaseFunctionRegistry` class Example runs: Custom advantage (mean of reward) <img width="956" height="326" alt="image" src="https://github.com/user-attachments/assets/1b7222bc-fbb9-49b1-876d-265b71201087" /> Custom policy loss (reinforce - just (-logprobs * advantages).mean()) <img width="939" height="330" alt="image" src="https://github.com/user-attachments/assets/cbed7ef5-b3e7-4e32-beba-b52b80879f47" />
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