Learning to act optimally in partially observable Markov decision processes using hybrid probabilistic logic programs

Abstract

We present a probabilistic logic programming framework to reinforcement learning, by integrating reinforcement learning, in POMDP environments, with normal hybrid probabilistic logic programs with probabilistic answer set semantics, that is capable of representing domain-specific knowledge. We formally prove the correctness of our approach. We show that the complexity of finding a policy for a reinforcement learning problem in our approach is NP-complete. In addition, we show that any reinforcement learning problem can be encoded as a classical logic program with answer set semantics. We also show that a reinforcement learning problem can be encoded as a SAT problem. We present a new high level action description language that allows the factored representation of POMDP. Moreover, we modify the original model of POMDP so that it be able to distinguish between knowledge producing actions and actions that change the environment. © 2011 Springer-Verlag.