Feature Markov Decision Processes

Details Feature Markov Decision Processes Feature Markov Decision Processes

2008-12-25

arxiv.org/abs/0812.4580v1

Proc. 2nd Conf. on Artificial General Intelligence (AGI 2009) pages 61-66

Computer Science

Artificial Intelligence

7 pages

Scientific paper

General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). So far it is an art performed by human designers to extract the right state representation out of the bare observations, i.e. to reduce the agent setup to the MDP framework. Before we can think of mechanizing this search for suitable MDPs, we need a formal objective criterion. The main contribution of this article is to develop such a criterion. I also integrate the various parts into one learning algorithm. Extensions to more realistic dynamic Bayesian networks are developed in a companion article.

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