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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
81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
"""
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Module that defines the LSTM middleware class
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"""
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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 rnn
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from rl_coach.architectures.mxnet_components.layers import Dense
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from rl_coach.architectures.mxnet_components.middlewares.middleware import Middleware
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from rl_coach.architectures.middleware_parameters import LSTMMiddlewareParameters
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from rl_coach.base_parameters import MiddlewareScheme
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nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol]
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class LSTMMiddleware(Middleware):
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def __init__(self, params: LSTMMiddlewareParameters):
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"""
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LSTMMiddleware or Long Short Term Memory Middleware can be used in the middle part of the network. It takes the
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embeddings from the input embedders, after they were aggregated in some method (for example, concatenation)
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and passes it through a neural network which can be customizable but shared between the heads of the network.
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:param params: parameters object containing batchnorm, activation_function, dropout and
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number_of_lstm_cells properties.
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"""
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super(LSTMMiddleware, self).__init__(params)
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self.number_of_lstm_cells = params.number_of_lstm_cells
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with self.name_scope():
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self.lstm = rnn.LSTM(hidden_size=self.number_of_lstm_cells)
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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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Middleware. Are used to create Block when LSTMMiddleware is initialised, and are applied before the LSTM.
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:return: dictionary of schemes, with key of type MiddlewareScheme enum and value being list of mxnet.gluon.Block.
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"""
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return {
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MiddlewareScheme.Empty:
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[],
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# Use for PPO
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MiddlewareScheme.Shallow:
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[
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Dense(units=64)
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],
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# Use for DQN
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MiddlewareScheme.Medium:
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[
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Dense(units=512)
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],
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MiddlewareScheme.Deep:
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[
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Dense(units=128),
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Dense(units=128),
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Dense(units=128)
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]
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}
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def hybrid_forward(self,
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F: ModuleType,
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x: nd_sym_type,
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*args, **kwargs) -> nd_sym_type:
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"""
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Used for forward pass through LSTM middleware network.
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Applies dense layers from selected scheme before passing result to LSTM layer.
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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: state embedding, of shape (batch_size, in_channels).
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:return: state middleware embedding, where shape is (batch_size, channels).
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"""
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x_ntc = x.reshape(shape=(0, 0, -1))
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emb_ntc = super(LSTMMiddleware, self).hybrid_forward(F, x_ntc, *args, **kwargs)
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emb_tnc = emb_ntc.transpose(axes=(1, 0, 2))
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return self.lstm(emb_tnc)
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