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We present a probabilistic logic programming framework to reinforcementlearning, by integrating reinforce-ment learning, in POMDP environments, withnormal hybrid probabilistic logic programs with probabilistic answer setseman-tics, that is capable of representing domain-specific knowledge. Weformally prove the correctness of our approach. We show that the complexity offinding a policy for a reinforcement learning problem in our approach isNP-complete. In addition, we show that any reinforcement learning problem canbe encoded as a classical logic program with answer set semantics. We also showthat a reinforcement learning problem can be encoded as a SAT problem. Wepresent a new high level action description language that allows the factoredrepresentation of POMDP. Moreover, we modify the original model of POMDP sothat it be able to distinguish between knowledge producing actions and actionsthat change the environment.

Reinforcement Learning in Partially Observable Markov Decision Processes using Hybrid Probabilistic Logic Programs