State Space Segmentation for Acquisition of Agent Behavior
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Fuzzy Q-Learning with the modified fuzzy ART neural network
Web Intelligence and Agent Systems
State space segmentation for acquisition of agent behavior
Web Intelligence and Agent Systems
Scaling Up Multi-agent Reinforcement Learning in Complex Domains
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Direct code access in self-organizing neural networks for reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A hybrid agent architecture integrating desire, intention and reinforcement learning
Expert Systems with Applications: An International Journal
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This paper presents a self-organizing cognitive architecture, known as TD-FALCON, that learns to function through its interaction with the environment. TD-FALCON learns the value functions of the state-action space estimated through a temporal difference (TD) method. The learned value functions are then used to determine the optimal actions based on an action selection policy. We present a specific instance of TD-FALCON based on an e-greedy action policy and a Q-learning value estimation formula. Experiments based on a minefield navigation task and a minefield pursuit task show that TD-FALCON systems are able to adapt and function well in a multi-agent environment without an explicit mechanism for collaboration.