c91def6cbc
Cherry-pick from #200 - update the function signature - format code-style - move _compile into separate functions - fix a bug in to_torch and to_numpy (Batch) - remove None in action_range In short, the code-format only contains function-signature style and `'` -> `"`. (pick up from [black](https://github.com/psf/black))
85 lines
2.3 KiB
Python
85 lines
2.3 KiB
Python
import numpy as np
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from abc import ABC, abstractmethod
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from typing import Union, Optional, Sequence
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class BaseNoise(ABC, object):
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"""The action noise base class."""
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def __init__(self) -> None:
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super().__init__()
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@abstractmethod
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def __call__(self, size: Sequence[int]) -> np.ndarray:
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"""Generate new noise."""
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raise NotImplementedError
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def reset(self) -> None:
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"""Reset to the initial state."""
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pass
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class GaussianNoise(BaseNoise):
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"""The vanilla gaussian process, for exploration in DDPG by default."""
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def __init__(self, mu: float = 0.0, sigma: float = 1.0) -> None:
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super().__init__()
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self._mu = mu
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assert 0 <= sigma, "Noise std should not be negative."
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self._sigma = sigma
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def __call__(self, size: Sequence[int]) -> np.ndarray:
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return np.random.normal(self._mu, self._sigma, size)
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class OUNoise(BaseNoise):
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"""Class for Ornstein-Uhlenbeck process, as used for exploration in DDPG.
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Usage:
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::
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# init
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self.noise = OUNoise()
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# generate noise
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noise = self.noise(logits.shape, eps)
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For required parameters, you can refer to the stackoverflow page. However,
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our experiment result shows that (similar to OpenAI SpinningUp) using
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vanilla gaussian process has little difference from using the
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Ornstein-Uhlenbeck process.
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"""
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def __init__(
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self,
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mu: float = 0.0,
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sigma: float = 0.3,
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theta: float = 0.15,
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dt: float = 1e-2,
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x0: Optional[Union[float, np.ndarray]] = None,
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) -> None:
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super().__init__()
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self._mu = mu
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self._alpha = theta * dt
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self._beta = sigma * np.sqrt(dt)
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self._x0 = x0
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self.reset()
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def __call__(
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self, size: Sequence[int], mu: Optional[float] = None
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) -> np.ndarray:
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"""Generate new noise.
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Return an numpy array which size is equal to ``size``.
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"""
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if self._x is None or self._x.shape != size:
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self._x = 0.0
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if mu is None:
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mu = self._mu
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r = self._beta * np.random.normal(size=size)
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self._x = self._x + self._alpha * (mu - self._x) + r
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return self._x
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def reset(self) -> None:
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"""Reset to the initial state."""
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self._x = self._x0
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