Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Reinforcement learning with hierarchies of machines
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Minimization algorithms for sequential transducers
Theoretical Computer Science
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
A Platform for Combining Deductive with Algorithmic Verification
CAV '96 Proceedings of the 8th International Conference on Computer Aided Verification
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
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We propose a method for generating and learning agent controllers, which combines techniques from automated planning and reinforcement learning. An incomplete description of the domain is first used to generate a non-deterministic automaton able to act (sub-optimally) in the given environment. Such a controller is then refined through experience, by learning choices at non-deterministic points. On the one hand, the incompleteness of the model, which would make a pure-planning approach ineffective, is overcome through learning. On the other hand, the portion of the domain available drives the learning process, that otherwise would be excessively expensive. Our method allows to adapt the behavior of a given planner to the environment, facing the unavoidable discrepancies between the model and the environment. We provide quantitative experiments with a simulator of a mobile robot to assess the performance of the proposed method.