Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Hierarchical control and learning for markov decision processes
Hierarchical control and learning for markov decision processes
Temporal abstraction in reinforcement learning
Temporal abstraction in reinforcement learning
Discovering hierarchy in reinforcement learning
Discovering hierarchy in reinforcement learning
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Model minimization in Markov decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
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Hierarchical reinforcement learning methods have not been able to simultaneously abstract and reuse subtasks with discounted value functions. The contribution of this paper is to introduce two completion functions that jointly decompose the value function hierarchically to solve this problem. The significance of this result is that the benefits of hierarchical reinforcement learning can be extended to discounted value functions and to continuing (infinite horizon) reinforcement learning problems. This paper demonstrates the method with the an algorithm that discovers subtasks automatically. An example is given where the optimum policy requires a subtask never to terminate.