6 research outputs found
A brief history of learning classifier systems: from CS-1 to XCS and its variants
© 2015, Springer-Verlag Berlin Heidelberg. 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) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning
XCS Classifier System with Experience Replay
XCS constitutes the most deeply investigated classifier system today. It
bears strong potentials and comes with inherent capabilities for mastering a
variety of different learning tasks. Besides outstanding successes in various
classification and regression tasks, XCS also proved very effective in certain
multi-step environments from the domain of reinforcement learning. Especially
in the latter domain, recent advances have been mainly driven by algorithms
which model their policies based on deep neural networks -- among which the
Deep-Q-Network (DQN) is a prominent representative. Experience Replay (ER)
constitutes one of the crucial factors for the DQN's successes, since it
facilitates stabilized training of the neural network-based Q-function
approximators. Surprisingly, XCS barely takes advantage of similar mechanisms
that leverage stored raw experiences encountered so far. To bridge this gap,
this paper investigates the benefits of extending XCS with ER. On the one hand,
we demonstrate that for single-step tasks ER bears massive potential for
improvements in terms of sample efficiency. On the shady side, however, we
reveal that the use of ER might further aggravate well-studied issues not yet
solved for XCS when applied to sequential decision problems demanding for
long-action-chains
Learning classifier systems from first principles: A probabilistic reformulation of learning classifier systems from the perspective of machine learning
Learning Classifier Systems (LCS) are a family of rule-based machine learning methods. They aim at the autonomous production of potentially human readable results that are the most compact generalised representation whilst also maintaining high predictive accuracy, with a wide range of application areas, such as autonomous robotics, economics, and multi-agent systems. Their design is mainly approached heuristically and, even though their performance is competitive in regression and classification tasks, they do not meet their expected performance in sequential decision tasks despite being initially designed for such tasks. It is out contention that improvement is hindered by a lack of theoretical understanding of their underlying mechanisms and dynamics.EThOS - Electronic Theses Online ServiceGBUnited Kingdo