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Authors: B. K. Swathi Prasad ; Aditya G. Manjunath and Hariharan Ramasangu

Affiliation: M. S. Ramaiah University of Applied Sciences, India

ISBN: 978-989-758-219-6

Keyword(s): Formation, Pattern, Q-learning, Algorithm, Episode.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bioinformatics ; Biomedical Engineering ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Methodologies and Technologies ; Multi-Agent Systems ; Operational Research ; Physical Agents ; Simulation ; Software Engineering ; State Space Search ; Symbolic Systems

Abstract: This work provides details a simulation experiment and analysis of Q-learning applied to multi-agent systems. Six agents interact within the environment to form hexagon, square and triangle, by reaching their specific goal states. In the proposed approach, the agents form a hexagon and the maximum dimension of this pattern is be reduced to form patterns with smaller dimensions. A decentralised approach of controlling the agents via Q-Learning was adopted which reduced complexity. The agents will be able to either move forward, backward and sideways based on the decision taken. Finally, the Q-Learning action-reward system was designed such that the agents could exploit the system which meant that they would earn high rewards for correct actions and negative rewards so the opposite.

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Paper citation in several formats:
Prasad, B.; Manjunath, A. and Ramasangu, H. (2017). Multi-agent Polygon Formation using Reinforcement Learning.In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-219-6, pages 159-165. DOI: 10.5220/0006187001590165

author={B. K. Swathi Prasad. and Aditya G. Manjunath. and Hariharan Ramasangu.},
title={Multi-agent Polygon Formation using Reinforcement Learning},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},


JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Multi-agent Polygon Formation using Reinforcement Learning
SN - 978-989-758-219-6
AU - Prasad, B.
AU - Manjunath, A.
AU - Ramasangu, H.
PY - 2017
SP - 159
EP - 165
DO - 10.5220/0006187001590165

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