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https://github.com/vale981/ray
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99 lines
3.3 KiB
Python
99 lines
3.3 KiB
Python
import numpy as np
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from ray.rllib.models.utils import get_activation_fn
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from ray.rllib.utils.framework import try_import_torch, TensorType
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torch, nn = try_import_torch()
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class NoisyLayer(nn.Module):
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"""A Layer that adds learnable Noise to some previous layer's outputs.
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Consists of:
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- a common dense layer: y = w^{T}x + b
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- a noisy layer: y = (w + \\epsilon_w*\\sigma_w)^{T}x +
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(b+\\epsilon_b*\\sigma_b)
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, where \epsilon are random variables sampled from factorized normal
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distributions and \\sigma are trainable variables which are expected to
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vanish along the training procedure.
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"""
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def __init__(
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self, in_size: int, out_size: int, sigma0: float, activation: str = "relu"
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):
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"""Initializes a NoisyLayer object.
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Args:
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in_size: Input size for Noisy Layer
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out_size: Output size for Noisy Layer
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sigma0: Initialization value for sigma_b (bias noise)
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activation: Non-linear activation for Noisy Layer
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"""
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super().__init__()
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self.in_size = in_size
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self.out_size = out_size
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self.sigma0 = sigma0
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self.activation = get_activation_fn(activation, framework="torch")
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if self.activation is not None:
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self.activation = self.activation()
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sigma_w = nn.Parameter(
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torch.from_numpy(
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np.random.uniform(
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low=-1.0 / np.sqrt(float(self.in_size)),
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high=1.0 / np.sqrt(float(self.in_size)),
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size=[self.in_size, out_size],
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)
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).float()
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)
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self.register_parameter("sigma_w", sigma_w)
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sigma_b = nn.Parameter(
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torch.from_numpy(
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np.full(
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shape=[out_size], fill_value=sigma0 / np.sqrt(float(self.in_size))
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)
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).float()
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)
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self.register_parameter("sigma_b", sigma_b)
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w = nn.Parameter(
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torch.from_numpy(
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np.full(
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shape=[self.in_size, self.out_size],
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fill_value=6 / np.sqrt(float(in_size) + float(out_size)),
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)
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).float()
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)
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self.register_parameter("w", w)
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b = nn.Parameter(torch.from_numpy(np.zeros([out_size])).float())
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self.register_parameter("b", b)
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def forward(self, inputs: TensorType) -> TensorType:
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epsilon_in = self._f_epsilon(
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torch.normal(
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mean=torch.zeros([self.in_size]), std=torch.ones([self.in_size])
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).to(inputs.device)
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)
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epsilon_out = self._f_epsilon(
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torch.normal(
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mean=torch.zeros([self.out_size]), std=torch.ones([self.out_size])
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).to(inputs.device)
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)
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epsilon_w = torch.matmul(
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torch.unsqueeze(epsilon_in, -1), other=torch.unsqueeze(epsilon_out, 0)
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)
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epsilon_b = epsilon_out
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action_activation = (
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torch.matmul(inputs, self.w + self.sigma_w * epsilon_w)
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+ self.b
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+ self.sigma_b * epsilon_b
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)
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if self.activation is not None:
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action_activation = self.activation(action_activation)
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return action_activation
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def _f_epsilon(self, x: TensorType) -> TensorType:
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return torch.sign(x) * torch.pow(torch.abs(x), 0.5)
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