wehub-resource-sync
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
from typing import TYPE_CHECKING, Any, Dict, List
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from ray.rllib.core.learner.torch.torch_meta_learner import TorchMetaLearner
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.framework import try_import_torch
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from ray.rllib.utils.typing import ModuleID, TensorType
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if TYPE_CHECKING:
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from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
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torch, nn = try_import_torch()
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class MAMLTorchMetaLearner(TorchMetaLearner):
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"""A `TorchMetaLearner` to perform MAML learning.
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This `TorchMetaLearner`
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- defines a MSE loss for learning simple (here non-linear) prediction.
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"""
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@override(TorchMetaLearner)
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def compute_loss_for_module(
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self,
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*,
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module_id: ModuleID,
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config: "AlgorithmConfig",
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batch: Dict[str, Any],
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fwd_out: Dict[str, TensorType],
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others_loss_per_module: List[Dict[ModuleID, TensorType]] = None,
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) -> TensorType:
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"""Defines a simple MSE prediction loss for continuous task.
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Note, MAML does not need the losses from the registered differentiable
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learners (contained in `others_loss_per_module`) b/c it computes a test
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loss on an unseen data batch.
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"""
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# Use a simple MSE loss for the meta learning task.
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return torch.nn.functional.mse_loss(fwd_out["y_pred"], batch["y"])
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