NDLI: Self-Organizing Neural Architectures and Cooperative Learning in a Multiagent Environment

Content Provider	IEEE Xplore Digital Library
Author	Dan Xiao Ah-Hwee Tan
Copyright Year	1996
Abstract	Temporal-difference-fusion architecture for learning, cognition, and navigation (TD-FALCON) is a generalization of adaptive resonance theory (a class of self-organizing neural networks) that incorporates TD methods for real-time reinforcement learning. In this paper, we investigate how a team of TD-FALCON networks may cooperate to learn and function in a dynamic multiagent environment based on minefield navigation and a predator/prey pursuit tasks. Experiments on the navigation task demonstrate that TD-FALCON agent teams are able to adapt and function well in a multiagent environment without an explicit mechanism of collaboration. In comparison, traditional Q-learning agents using gradient-descent-based feedforward neural networks, trained with the standard backpropagation and the resilient-propagation (RPROP) algorithms, produce a significantly poorer level of performance. For the predator/prey pursuit task, we experiment with various cooperative strategies and find that a combination of a high-level compressed state representation and a hybrid reward function produces the best results. Using the same cooperative strategy, the TD-FALCON team also outperforms the RPROP-based reinforcement learners in terms of both task completion rate and learning efficiency.
Page Count	14
File Size	598625
Starting Page	1567
Ending Page	1580
File Format	PDF
ISSN	10834419
Volume Number	37
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Navigation Neural networks Learning Autonomous agents Cognition Resonance Collaboration Feedforward neural networks Backpropagation algorithms Multiagent systems self-organizing neural architectures Multiagent cooperative learning reinforcement learning (RL)
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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