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5fadb9c18e
Adding mxnet components to rl_coach architectures. - Supports PPO and DQN - Tested with CartPole_PPO and CarPole_DQN - Normalizing filters don't work right now (see #49) and are disabled in CartPole_PPO preset - Checkpointing is disabled for MXNet
72 lines
3.3 KiB
Python
72 lines
3.3 KiB
Python
from typing import Union
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from types import ModuleType
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import mxnet as mx
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from mxnet.gluon import nn
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from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
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from rl_coach.architectures.mxnet_components.layers import convert_layer
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from rl_coach.base_parameters import EmbedderScheme
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nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol]
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class InputEmbedder(nn.HybridBlock):
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def __init__(self, params: InputEmbedderParameters):
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"""
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An input embedder is the first part of the network, which takes the input from the state and produces a vector
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embedding by passing it through a neural network. The embedder will mostly be input type dependent, and there
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can be multiple embedders in a single network.
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:param params: parameters object containing input_clipping, input_rescaling, batchnorm, activation_function
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and dropout properties.
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"""
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super(InputEmbedder, self).__init__()
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self.embedder_name = params.name
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self.input_clipping = params.input_clipping
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self.scheme = params.scheme
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with self.name_scope():
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self.net = nn.HybridSequential()
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if isinstance(self.scheme, EmbedderScheme):
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blocks = self.schemes[self.scheme]
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else:
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# if scheme is specified directly, convert to MX layer if it's not a callable object
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# NOTE: if layer object is callable, it must return a gluon block when invoked
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blocks = [convert_layer(l) for l in self.scheme]
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for block in blocks:
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self.net.add(block())
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if params.batchnorm:
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self.net.add(nn.BatchNorm())
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if params.activation_function:
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self.net.add(nn.Activation(params.activation_function))
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if params.dropout:
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self.net.add(nn.Dropout(rate=params.dropout))
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@property
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def schemes(self) -> dict:
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"""
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Schemes are the pre-defined network architectures of various depths and complexities that can be used for the
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InputEmbedder. Should be implemented in child classes, and are used to create Block when InputEmbedder is
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initialised.
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:return: dictionary of schemes, with key of type EmbedderScheme enum and value being list of mxnet.gluon.Block.
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"""
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raise NotImplementedError("Inheriting embedder must define schemes matching its allowed default "
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"configurations.")
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def hybrid_forward(self, F: ModuleType, x: nd_sym_type, *args, **kwargs) -> nd_sym_type:
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"""
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Used for forward pass through embedder network.
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:param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized).
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:param x: environment state, where first dimension is batch_size, then dimensions are data type dependent.
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:return: embedding of environment state, where shape is (batch_size, channels).
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"""
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# `input_rescaling` and `input_offset` set on inheriting embedder
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x = x / self.input_rescaling
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x = x - self.input_offset
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if self.input_clipping is not None:
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x.clip(a_min=self.input_clipping[0], a_max=self.input_clipping[1])
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x = self.net(x)
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return x.flatten()
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