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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

DECAF: Deep Case-based Policy Inference for knowledge transfer in Reinforcement Learning

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Author(s):
Glatt, Ruben [1] ; Da Silva, Felipe Leno [1] ; da Costa Bianchi, Reinaldo Augusto [2] ; Reali Costa, Anna Helena [1]
Total Authors: 4
Affiliation:
[1] Univ Sao Paulo, Av Prof Luciano Gualberto 158, BR-05508010 Sao Paulo - Brazil
[2] FEIs Univ Ctr, Av Humberto Alencar Castelo Branco 3972, BR-09850901 Sao Bernardo Do Campo, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: EXPERT SYSTEMS WITH APPLICATIONS; v. 156, OCT 15 2020.
Web of Science Citations: 0
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 formulate 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 training 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. (C) 2020 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 16/21047-3 - ALIS: Autonomous Learning in Intelligent System
Grantee:Anna Helena Reali Costa
Support type: Regular Research Grants
FAPESP's process: 15/16310-4 - Transfer Learning in Reinforcement Learning Multi-Agent Systems
Grantee:Felipe Leno da Silva
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 18/00344-5 - Reusing previous task solutions in multiagent reinforcement learning
Grantee:Felipe Leno da Silva
Support type: Scholarships abroad - Research Internship - Doctorate
FAPESP's process: 16/18792-9 - Describing, representing and solving spatial puzzles
Grantee:Paulo Eduardo Santos
Support type: Research Grants - Research Partnership for Technological Innovation - PITE