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KUCHAŘ, K. HOLASOVÁ, E. HRBOTICKÝ, L. RAJNOHA, M. BURGET, R.
Original Title
Supervised Learning in Multi-Agent Environments Using Inverse Point of View
Type
conference paper
Language
English
Original Abstract
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.
Keywords
artificial intelligence; machine learning; multiagent systems
Authors
KUCHAŘ, K.; HOLASOVÁ, E.; HRBOTICKÝ, L.; RAJNOHA, M.; BURGET, R.
Released
1. 7. 2019
ISBN
978-1-7281-1864-2
Book
Proceedings of the 2019 42nd International Conference on Telecommunications and Signal Processing (TSP)
Pages from
625
Pages to
628
Pages count
4
URL
https://ieeexplore.ieee.org/document/8768860
BibTex
@inproceedings{BUT157572, author="Karel {Kuchař} and Eva {Holasová} and Lukáš {Hrbotický} and Martin {Rajnoha} and Radim {Burget}", title="Supervised Learning in Multi-Agent Environments Using Inverse Point of View", booktitle="Proceedings of the 2019 42nd International Conference on Telecommunications and Signal Processing (TSP)", year="2019", pages="625--628", doi="10.1109/TSP.2019.8768860", isbn="978-1-7281-1864-2", url="https://ieeexplore.ieee.org/document/8768860" }