Reinforcement Learning with Convolutional Reservoir Computing

TL;DR

This paper introduces RCRC, a reinforcement learning approach that uses fixed random-weight CNNs and reservoir computing to efficiently extract features and decide actions without extensive training or data storage.

Contribution

The study presents a novel RCRC model that eliminates the need for training feature extractors and storing data, simplifying reinforcement learning processes.

Findings

RCRC can solve multiple tasks with a single feature extractor.

The model requires only one task-dependent weight to be trained.

It achieves high performance without training CNNs or storing data.

Abstract

Recently, reinforcement learning models have achieved great success, mastering complex tasks such as Go and other games with higher scores than human players. Many of these models store considerable data on the tasks and achieve high performance by extracting visual and time-series features using convolutional neural networks (CNNs) and recurrent neural networks, respectively. However, these networks have very high computational costs because they need to be trained by repeatedly using the stored data. In this study, we propose a novel practical approach called reinforcement learning with convolutional reservoir computing (RCRC) model. The RCRC model uses a fixed random-weight CNN and a reservoir computing model to extract visual and time-series features. Using these extracted features, it decides actions with an evolution strategy method. Thereby, the RCRC model has several desirable features: (1) there is no need to train the feature extractor, (2) there is no need to store training data, (3) it can take a wide range of actions, and (4) there is only a single task-dependent weight parameter to be trained. Furthermore, we show the RCRC model can solve multiple reinforcement learning tasks with a completely identical feature extractor.