Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …

A fundamental aspect of intelligent agent behaviour is the ability to encode salient features
of experience in memory and use these memories, in combination with current sensory …

The direction set by Wilson's XCS is that modern Learning Classifier Systems can be
characterized by their use of rule accuracy as the utility metric for the search algorithm (s) …

Autoencoders are data-specific compression algorithms learned automatically from
examples. The predominant approach has been to construct single large global models that …

Tangled Program Graphs (TPG) is a framework for genetic programming which has shown
promise in challenging reinforcement learning problems with discrete action spaces. The …

This article presents the first results from using a learning classifier system capable of
performing adaptive computation with deep neural networks. Individual classifiers within the …

A number of representation schemes have been presented for use within learning classifier
systems, ranging from binary encodings to neural networks. This paper presents results from …

Modern learning classifier systems typically exploit a niched genetic algorithm to facilitate
rule discovery. When used for reinforcement learning, such rules represent generalisations …

Accuracy-based XCS classifier system has been shown to evolve classifiers with accurate
and maximally general characteristics. XCS generally represents its classifiers with binary …