Reinforcement learning algorithms for autonomous adaptive agents

Padakandla, S (2020) Reinforcement learning algorithms for autonomous adaptive agents. In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, 19 May 2020 through 13 May 2020, Virtual, Auckland, pp. 2201-2203.

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Abstract

Intelligent agents are being designed to automate many tasks - for e.g., traffic signal control, vehicle driving, inventory control and are also being used in improving lives of people - like in healthcare, agriculture, wildlife protection etc. The widespread deployment of intelligent agents requires that we minimize the bottlenecks which affect their performance and utility. Motivated by this challenge, my thesis proposes new algorithms and methods which helps the agent in efficiently operating in the real-world and also during interaction with humans. My work has shown significant improvements in the performance of deployed agents, when operating in real world. © 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.

Item Type:	Conference Paper
Publication:	Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Publisher:	International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Additional Information:	The copyright of the article belongs to International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Keywords:	Agricultural robots; Intelligent agents; Inventory control; Learning algorithms; Multi agent systems; Reinforcement learning; Street traffic control; Traffic signals, Adaptive agents; Real-world; Traffic signal control; Wildlife protection, Autonomous agents
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	22 Dec 2020 10:30
Last Modified:	09 Dec 2022 10:13
URI:	https://eprints.iisc.ac.in/id/eprint/67216

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