NDLI: A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

Content Provider	IEEE Xplore Digital Library
Author	Van Moffaert, K. Brys, T. Chandra, A. Esterle, L. Lewis, P.R. Nowe, A.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Vrije Univ. Brussel, Brussels, Belgium (Van Moffaert, K.; Brys, T.; Nowe, A.) \|\| Aston Univ., Birmingham, UK (Lewis, P.R.) \|\| Univ. of Oslo, Oslo, Norway (Chandra, A.) \|\| Lakeside Labs., Alpen-Adria Univ. Klagenfurt, Klagenfurt, Austria (Esterle, L.)
Abstract	To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.
Starting Page	2306
Ending Page	2314
File Size	5279292
Page Count	9
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889637
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cameras Learning (artificial intelligence) Space exploration Optimization Smart cameras Algorithm design and analysis Search problems
Content Type	Text
Resource Type	Article

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