An Approach of Temporal Difference Learning Using Agent-Oriented Programming
Amelia Bădică, Costin Bădică, Mirjana Drenovak Ivanović, Dejan M. Mitrović · 2015
Reinforcement Learning -- RL is an important agent problem that was not approached using the tools provided by Agent Oriented Programming -- AOP. Agent Speak (L) and its Jason implementation based on Java platform are representing state-of-the-art approaches of AOP based on the Belief-Desire-Intention -- BDI model. Temporal Difference Learning -- TDL is a passive RL method that can be used by an agent to learn its utility function while it is acting according to a given policy in an uncertain and dynamic environment. In this paper we present an approach for modeling and implementation of TDL using the Jason AOP language. So, our paper is presenting a contribution towards narrowing the gap between RL and AOP, by endowing BDI agents with TDL skills.