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Multi-objective fuzzy Q-learning to solve continuous state-action problems
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AI
DOI：10.1016/j.neucom.2022.10.035.png)
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摘要
En 中文
Many real world problems are multi-objective. Thus, the need for multi-objective learning and optimiza-tion algorithms is inevitable. Although the multi-objective optimization algorithms are well-studied, the multi-objective learning algorithms have attracted less attention. In this paper, a fuzzy multi-objective reinforcement learning algorithm is proposed, and we refer to it as the multi-objective fuzzy Q-learning (MOFQL) algorithm. The algorithm is implemented to solve a bi-objective reach-avoid game. The majority of the multi-objective reinforcement algorithms proposed address solving problems in the discrete state-action domain. However, the MOFQL algorithm can also handle problems in a contin-uous state-action domain. A fuzzy inference system (FIS) is implemented to estimate the value function for the bi-objective problem. We used a temporal difference (TD) approach to update the fuzzy rules. The proposed method isa multi-policy multi-objective algorithm and can find the non-convex regions of the Pareto front.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Reinforcement learning
Differential games
Q-learning
Multi-objective reinforcement learning
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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