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Discovering gated recurrent neural network architectures
Reinforcement Learning agent networks with memory are a key component in solving POMDP tasks.
Gated recurrent networks such as those composed of Long Short-Term
Memory (LSTM) nodes have recently been used to improve
state of the art in many supervised sequential processing tasks such as speech
recognition and machine translation. However, scaling them to deep
memory tasks in reinforcement learning domain is challenging because of sparse and deceptive
reward function. To address this challenge first, a new secondary optimization objective is introduced
that maximizes the information (Info-max) stored in
the LSTM network. Results indicate that when combined with neuroevolution, Info-max can discover powerful
LSTM-based memory solutions that outperform traditional
RNNs. Next, for the supervised learning tasks, neuroevolution techniques are employed
to design new LSTM architectures. Such architectural variations include
discovering new pathways between the recurrent layers as well as designing new gated
recurrent nodes. This dissertation proposes evolution of a tree-based
encoding of the gated memory nodes, and shows that it makes
it possible to explore new variations more effectively than other
methods. The method discovers nodes with multiple recurrent paths
and multiple memory cells, which lead to significant improvement
in the standard language modeling benchmark task. The dissertation also
shows how the search process can be speeded up by training an
LSTM network to estimate performance of candidate structures, and
by encouraging exploration of novel solutions. Thus, evolutionary
design of complex neural network structures promises to improve
performance of deep learning architectures beyond human ability
to do so.Computer Science