Relational action model learning and planning integration Relational action model learning and planning integration
نویسندگان
چکیده
Résumé : This paper addresses a relational reinforcement learning-like problem with general action-model relational learning and planning techniques. We propose an integrated system for both action model learning and action selection in the context of adaptive behavior of autonomous agents. Learning is incremental. It operates with relational representations and produces disjunctions of 1st order conjunctive rules. Actions include greedy goal driven exploitation of what has been learned so far, as well as exploration actions. Exploration is active : the actions are chosen in order to improve the action model. We also show how relational representations permits straightforward transfer learning. Mots-clés : online and incremental learning, action model, relational reinforcement learning, inductive logic programming, planning
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