Guiding a relational learning agent with a learning classifier system

This paper researches a collaborative strategy between an XCS learning classifier system (LCS) and a relational learning (RL) agent. The problem here is to learn a relational policy for a stochastic markovian decision process. In the proposed method the XCS agent is used to improve the performance of the RL agent by filtering the samples used at the induction step. This research shows that in these conditions, one of the main benefits of using the XCS algorithm comes from selecting the examples for relational learning using an estimation for the accuracy of the predicted value at each state-action pair. This kind of transfer learning is important because the characteristics of both agents are complementary: the RL agent incrementally induces a high level description of a policy, while the LCS agent offers adaptation to changes in the environment.