wehub-resource-sync
94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
"""
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[1] Mastering Diverse Domains through World Models - 2023
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D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
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https://arxiv.org/pdf/2301.04104v1.pdf
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[2] Mastering Atari with Discrete World Models - 2021
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D. Hafner, T. Lillicrap, M. Norouzi, J. Ba
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https://arxiv.org/pdf/2010.02193.pdf
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"""
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from typing import Optional
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from ray.rllib.algorithms.dreamerv3.torch.models.components import (
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dreamerv3_normal_initializer,
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)
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from ray.rllib.algorithms.dreamerv3.utils import (
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get_dense_hidden_units,
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get_num_dense_layers,
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)
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from ray.rllib.utils.framework import try_import_torch
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torch, nn = try_import_torch()
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class MLP(nn.Module):
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"""An MLP primitive used by several DreamerV3 components and described in [1] Fig 5.
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MLP=multi-layer perceptron.
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See Appendix B in [1] for the MLP sizes depending on the given `model_size`.
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"""
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def __init__(
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self,
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*,
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input_size: int,
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model_size: str = "XS",
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num_dense_layers: Optional[int] = None,
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dense_hidden_units: Optional[int] = None,
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output_layer_size=None,
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):
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"""Initializes an MLP instance.
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Args:
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input_size: The input size of the MLP.
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model_size: The "Model Size" used according to [1] Appendinx B.
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Use None for manually setting the different network sizes.
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num_dense_layers: The number of hidden layers in the MLP. If None,
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will use `model_size` and appendix B to figure out this value.
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dense_hidden_units: The number of nodes in each hidden layer. If None,
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will use `model_size` and appendix B to figure out this value.
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output_layer_size: The size of an optional linear (no activation) output
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layer. If None, no output layer will be added on top of the MLP dense
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stack.
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"""
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super().__init__()
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self.output_size = None
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num_dense_layers = get_num_dense_layers(model_size, override=num_dense_layers)
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dense_hidden_units = get_dense_hidden_units(
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model_size, override=dense_hidden_units
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)
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layers = []
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for _ in range(num_dense_layers):
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# In this order: layer, normalization, activation.
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linear = nn.Linear(input_size, dense_hidden_units, bias=False)
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# Use same initializers as the Author in their JAX repo.
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dreamerv3_normal_initializer(linear.weight)
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layers.append(linear)
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layers.append(nn.LayerNorm(dense_hidden_units, eps=0.001))
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layers.append(nn.SiLU())
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input_size = dense_hidden_units
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self.output_size = (dense_hidden_units,)
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self.output_layer = None
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if output_layer_size:
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linear = nn.Linear(input_size, output_layer_size, bias=True)
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# Use same initializers as the Author in their JAX repo.
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dreamerv3_normal_initializer(linear.weight)
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nn.init.zeros_(linear.bias)
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layers.append(linear)
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self.output_size = (output_layer_size,)
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self._net = nn.Sequential(*layers)
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def forward(self, input_):
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"""Performs a forward pass through this MLP.
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Args:
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input_: The input tensor for the MLP dense stack.
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"""
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return self._net(input_)
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