Supervised Learning in Multi-Agent Environments Using Inverse Point of View

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU132545" target="_blank" >RIV/00216305:26220/19:PU132545 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8768860" target="_blank" >https://ieeexplore.ieee.org/document/8768860</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP.2019.8768860" target="_blank" >10.1109/TSP.2019.8768860</a>

Result language
angličtina
Original language name
Supervised Learning in Multi-Agent Environments Using Inverse Point of View
Original language description
There are many approaches that are being used in multi-agent environment to learn agents’ behaviour. Semisupervised approaches such as reinforcement learning (RL) or genetic programming (GP) are one of the most frequently used. Disadvantage of these methods is they are relatively computational resources demanding, suffers from vanishing gradient during when machine learning approach is used and has often non-convex optimization function, which makes behaviour learning challenging. This paper introduces a method for data gathering for supervised machine learning using agent’s inverse point of view. Proposed method explores agent’s neighboring environment and collects data also from surrounding agents instead of traditional approaches that uses only agents’ sensors and knowledge. Advantage of this approach is, the collected data can be used with supervised machine learning, which is significantly less computationally demanding when compared to RL or GP. A proposed method was tested and demonstrated on Robocode game, where agents (i.e. tanks) were trained to avoid opponent tanks missiles.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 2019 42nd International Conference on Telecommunications and Signal Processing (TSP)
ISBN
978-1-7281-1864-2
ISSN
—
e-ISSN
—
Number of pages
4
Pages from-to
625-628
Publisher name
Neuveden
Place of publication
Neuveden
Event location
Budapest, Hungary
Event date
Jul 1, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000493442800137

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