CELIBERTO JUNIOR, L. A.MATSUURA, J. P.DE MANTARAS, R. L.Reinaldo Bianchi2022-01-122022-01-122011-07-02CELIBERTO JUNIOR, L. A.; MATSUURA, J. P.; DE MANTARAS, R. L.; BIANCHI, R. Using cases as heuristics in reinforcement learning: A transfer learning application. IJCAI International Joint Conference on Artificial Intelligence. p. 1211-1217, July, 2011.1045-0823https://repositorio.fei.edu.br/handle/FEI/4180In this paper we propose to combine three AI techniques to speed up a Reinforcement Learning algorithm in a Transfer Learning problem: Case-based 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.Acesso RestritoArtigo de evento10.5591/978-1-57735-516-8/IJCAI11-206