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Using Cases as Heuristics in Reinforcement Learning: A Transfer Learning Application

AuthorsCeliberto, Luiz A.; Matsuura, Jackson P.; López de Mántaras, Ramón ; Bianchi, Reinaldo
Issue Date2011
PublisherAAAI Press
Citation22d International Joint Conference on Artificial Intelligence (IJCAI 2011). Proceedings. Barcelona, July 16-22, (2011), pp.1211-1217.
AbstractIn this paper we propose to combine three AI techniques to speed up a Reinforcement Learning algorithm in a Transfer Learning problem: Casebased Reasoning, Heuristically Accelerated Reinforcement Learning and Neural Networks. To do so, we propose a new algorithm, called L3, which works in 3 stages: in the first stage, it uses Reinforcement Learning to learn how to perform one task, and stores the optimal policy for this problem as a case-base; in the second stage, it uses a Neural Network to map actions from one domain to actions in the other domain and; in the third stage, it uses the case-base learned in the first stage as heuristics to speed up the learning performance in a related, but different, task. The RL algorithm used in the first phase is the Q-learning and in the third phase is the recently proposed Case-based Heuristically Accelerated Q-learning. A set of empirical evaluations were conducted in transferring the learning between two domains, the Acrobot and the Robocup 3D: the policy learned during the solution of the Acrobot Problem is transferred and used to speed up the learning of stability policies for a humanoid robot in the Robocup 3D simulator. The results show that the use of this algorithm can lead to a significant improvement in the performance of the agent.
Identifiersisbn: 978-1-57735-512-0
Appears in Collections:(IIIA) Comunicaciones congresos
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