Instinct as an inductive bias for learning behavioral sequences
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Adding temporary memory to ZCS
Adaptive Behavior
Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Machine Learning - Special issue on robot learning
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolutionary Computation
Vision Based State Space Construction for Learning Mobile Robots in Multi-agent Environments
EWLR-6 Proceedings of the 6th European Workshop on Learning Robots
Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems
Learning Classifier Systems, From Foundations to Applications
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
A Self-Adaptive Classifier System
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Hierarchical control and learning for markov decision processes
Hierarchical control and learning for markov decision processes
Reinforcement Learning in Continuous Time and Space
Neural Computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Learning reactive and planning rules in a motivationally autonomousanimat
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Analysing Learning Classifier Systems in Reactive and Non-reactive Robotic Tasks
Learning Classifier Systems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
| Hi-index | 0.00 |
To date there have been few implementation of Holland's Learning Classifier System (LCS) on real robots. The paper introduces a Temporal Classifier System (TCS), an LCS derived from Wilson's ZCS. Traditional LCS have the ability to generalise over the state action-space of a reinforcement learning problem using evolutionary techniques. In TCS this generalisation ability can also be used to determine the state divisions in the state space considered by the LCS. TCS also implements components from Semi-Mark-Decision Process (SMDP) theory to weight the influence of time on the reward functions of the LCS. A simple light-seeking task on a real robot platform using TCS is presented which demonstrates desirable adaptive characteristics for the use of LCS on real robots.