Self-organizing agents for reinforcement learning in virtual worlds

We present a self-organizing neural model for creating intelligent learning agents in virtual worlds. As agents in a virtual world roam, interact and socialize with users and other agents as in real world without explicit goals and teachers, learning in virtual world presents many challenges not fou...

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Main Authors: KANG, Yilin, TAN, Ah-hwee
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Language:English
Published: Institutional Knowledge at Singapore Management University 2010
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Online Access:https://ink.library.smu.edu.sg/sis_research/6799
https://ink.library.smu.edu.sg/context/sis_research/article/7802/viewcontent/RL_Agent_IJCNN_2010.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-78022022-01-27T08:33:48Z Self-organizing agents for reinforcement learning in virtual worlds KANG, Yilin TAN, Ah-hwee We present a self-organizing neural model for creating intelligent learning agents in virtual worlds. As agents in a virtual world roam, interact and socialize with users and other agents as in real world without explicit goals and teachers, learning in virtual world presents many challenges not found in typical machine learning benchmarks. In this paper, we highlight the unique issues and challenges of building learning agents in virtual world using reinforcement learning. Specifically, a self-organizing neural model, named TD-FALCON (Temporal Difference - Fusion Architecture for Learning and Cognition), is deployed, which enables an autonomous agent to adapt and function in a dynamic environment with immediate as well as delayed evaluative feedback signals. We have implemented and evaluated TD-FALCON agents as virtual tour guides in a virtual world environment. Our experimental results show that the agents are able to adapt and improve their performance in real time. To the best of our knowledge, this is one of the few in-depth works on building complete learning agents that adapt their behaviors through real time reinforcement learning in virtual world. 2010-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6799 info:doi/10.1109/IJCNN.2010.5596363 https://ink.library.smu.edu.sg/context/sis_research/article/7802/viewcontent/RL_Agent_IJCNN_2010.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
KANG, Yilin
TAN, Ah-hwee
Self-organizing agents for reinforcement learning in virtual worlds
description We present a self-organizing neural model for creating intelligent learning agents in virtual worlds. As agents in a virtual world roam, interact and socialize with users and other agents as in real world without explicit goals and teachers, learning in virtual world presents many challenges not found in typical machine learning benchmarks. In this paper, we highlight the unique issues and challenges of building learning agents in virtual world using reinforcement learning. Specifically, a self-organizing neural model, named TD-FALCON (Temporal Difference - Fusion Architecture for Learning and Cognition), is deployed, which enables an autonomous agent to adapt and function in a dynamic environment with immediate as well as delayed evaluative feedback signals. We have implemented and evaluated TD-FALCON agents as virtual tour guides in a virtual world environment. Our experimental results show that the agents are able to adapt and improve their performance in real time. To the best of our knowledge, this is one of the few in-depth works on building complete learning agents that adapt their behaviors through real time reinforcement learning in virtual world.
format text
author KANG, Yilin
TAN, Ah-hwee
author_facet KANG, Yilin
TAN, Ah-hwee
author_sort KANG, Yilin
title Self-organizing agents for reinforcement learning in virtual worlds
title_short Self-organizing agents for reinforcement learning in virtual worlds
title_full Self-organizing agents for reinforcement learning in virtual worlds
title_fullStr Self-organizing agents for reinforcement learning in virtual worlds
title_full_unstemmed Self-organizing agents for reinforcement learning in virtual worlds
title_sort self-organizing agents for reinforcement learning in virtual worlds
publisher Institutional Knowledge at Singapore Management University
publishDate 2010
url https://ink.library.smu.edu.sg/sis_research/6799
https://ink.library.smu.edu.sg/context/sis_research/article/7802/viewcontent/RL_Agent_IJCNN_2010.pdf
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