Please use this identifier to cite or link to this item: https://repositorio.fei.edu.br/handle/FEI/3471
Title: DECAF: Deep Case-based Policy Inference for Knowledge Transfer in Reinforcement Learning
Authors: GLATT, RUBEN
SILVA, FELIPE LENO DA
Reinaldo Bianchi
COSTA, ANNA HELENA REALI
Issue Date: 15-Oct-2020
Abstract: Having the ability to solve increasingly complex problems using Reinforcement Learning (RL) has prompted researchers to start developing a greater interest in systematic approaches to retain and reuse knowledge over a variety of tasks. With Case-based Reasoning (CBR) there exists a general methodology that provides a framework for knowledge transfer which has been underrepresented in the RL literature so far. We for- mulate a terminology for the CBR framework targeted towards RL researchers with the goal of facilitating communication between the respective research communities. Based on this framework, we propose the Deep Case-based Policy Inference (DECAF) algorithm to accelerate learning by building a library of cases and reusing them if they are similar to a new task when training a new policy. DECAF guides the train- ing by dynamically selecting and blending policies according to their usefulness for the current target task, reusing previously learned policies for a more effective exploration but still enabling the adaptation to particularities of the new task. We show an empirical evaluation in the Atari game playing domain depicting the benefits of our algorithm with regards to sample efficiency, robustness against negative transfer, and performance increase when compared to state-of-the-art methods.
Keywords: Deep Reinforcement Learning
Case-based Reasoning
Transfer Learning
Knowledge discovery
Knowledge management
Neural networks
Journal: EXPERT SYSTEMS WITH APPLICATIONS
ISSN: 0957-4174
Citation: GLATT, R.; S., F. L. DA; BIANCHI, R. A. DA C.; COSTA, A. H R. DECAF: Deep Case-based Policy Inference for Knowledge Transfer in Reinforcement Learning. EXPERT SYSTEMS WITH APPLICATIONS, v. 1, p. 113420, 2020.
Access Type: Acesso Restrito
DOI: 10.1016/j.eswa.2020.113420
URI: https://repositorio.fei.edu.br/handle/FEI/3471
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