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- import numpy as np
- from ray.rllib.utils.framework import try_import_torch
- from ray.rllib.utils.metrics.stats.series import SeriesStats
- from ray.util.annotations import DeveloperAPI
- torch, _ = try_import_torch()
- @DeveloperAPI
- class MinStats(SeriesStats):
- """A Stats object that tracks the min of a series of singular values (not vectors)."""
- stats_cls_identifier = "min"
- def _np_reduce_fn(self, values: np.ndarray) -> float:
- return np.nanmin(values)
- def _torch_reduce_fn(self, values: "torch.Tensor"):
- """Reduce function for torch tensors (stays on GPU)."""
- # torch.nanmin not available, use workaround
- clean_values = values[~torch.isnan(values)]
- if len(clean_values) == 0:
- return torch.tensor(float("nan"), device=values.device)
- # Cast to float32 to avoid errors from Long tensors
- return torch.min(clean_values.float())
- def __repr__(self) -> str:
- return f"MinStats({self.peek()}; window={self._window}; len={len(self)})"
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