Please use this identifier to cite or link to this item:
Title: Reinforcement learning for swarm systems
Authors: Arumugam, Ramaswamy
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Arumugam, R. (2022). Reinforcement learning for swarm systems. Final Year Project (FYP), Nanyang Technological University, Singapore.
Abstract: The application of deep reinforcement learning to swarm systems is currently an actively explored topic. Adapting multi-agent reinforcement learning algorithms to swarm systems is difficult because of dynamic neighbourhood sizes and the lack of agent identities. Hence a key component to building a good Swarm RL algorithm is an information summarization module. There is currently no consensus on the best way to summarize the information from an agent's neighbourhood. Therefore we explore various techniques for information summarization. We evaluate these techniques on two tasks - cover and cluster. We also introduce a new method for summarization based on selecting the top K most important pieces of information from an agent's observation. In this paper, we provide an experimental study of our algorithm and its efficacy.
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
  Restricted Access
666.64 kBAdobe PDFView/Open

Page view(s)

Updated on Feb 28, 2024

Download(s) 50

Updated on Feb 28, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.