A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Generalization and scaling in reinforcement learning
Advances in neural information processing systems 2
Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Applications of the self-organising map to reinforcement learning
Neural Networks - New developments in self-organizing maps
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Hybrid learning approach based on adaptive resonance theory and reinforcement learning for computer generated agents
Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence)
Fuzzy Q-Learning with the Modified ART Neural Network
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Self-Organizing Cognitive Agents and Reinforcement Learning in Multi-Agent Environment
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Integrated cognitive architectures: a survey
Artificial Intelligence Review
Agent-augmented co-space: toward merging of real world and cyberspace
ATC'10 Proceedings of the 7th international conference on Autonomic and trusted computing
A hybrid agent architecture integrating desire, intention and reinforcement learning
Expert Systems with Applications: An International Journal
Knowledge-Based Exploration for Reinforcement Learning in Self-Organizing Neural Networks
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
| Hi-index | 0.00 |
TD-FALCON is a self-organizing neural network that incorporates Temporal Difference (TD) methods for reinforcement learning. Despite the advantages of fast and stable learning, TD-FALCON still relies on an iterative process to evaluate each available action in a decision cycle. To remove this deficiency, this paper presents a direct code access procedure whereby TD-FALCON conducts instantaneous searches for cognitive nodes that match with the current states and at the same time provide maximal reward values. Our comparative experiments show that TD-FALCON with direct code access produces comparable performance with the original TD-FALCON while improving significantly in computation efficiency and network complexity.