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arXiv · 1706.01417

A method for the online construction of the set of states of a Markov Decision Process using Answer Set Programming

Leonardo A. Ferreira·Reinaldo A. C. Bianchi·Paulo E. Santos·Ramon Lopez de Mantaras
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Abstract

Non-stationary domains, that change in unpredicted ways, are a challenge for agents searching for optimal policies in sequential decision-making problems. This paper presents a combination of Markov Decision Processes (MDP) with Answer Set Programming (ASP), named {\em Online ASP for MDP} (oASP(MDP)), which is a method capable of constructing the set of domain states while the agent interacts with a changing environment. oASP(MDP) updates previously obtained policies, learnt by means of Reinforcement Learning (RL), using rules that represent the domain changes observed by the agent. These rules represent a set of domain constraints that are processed as ASP programs reducing the search space. Results show that oASP(MDP) is capable of finding solutions for problems in non-stationary domains without interfering with the action-value function approximation process.

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Leonardo A. Ferreira, Reinaldo A. C. Bianchi, Paulo E. Santos, Ramon Lopez de Mantaras. 2017-06-05. A method for the online construction of the set of states of a Markov Decision Process using Answer Set Programming. https://arxiv.org/abs/1706.01417

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