Technical Note: \cal Q-Learning
Machine Learning
Multi-agent reinforcement learning: independent vs. cooperative agents
Readings in agents
Reinforcement Learning
Sparse Distributed Memory
Brains, Behavior and Robotics
Reinforcement Learning in POMDPs with Function Approximation
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Randomized Pursuit-Evasion in Graphs
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Randomized Pursuit-Evasion with Local Visibility
SIAM Journal on Discrete Mathematics
Fuzzy Kanerva-based function approximation for reinforcement learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Adaptive Fuzzy Function Approximation for Multi-agent Reinforcement Learning
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
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In this paper, we show how adaptive prototype optimization can be used to improve the performance of function approximation based on Kanerva Coding when solving largescale instances of classic multi-agent problems. We apply our techniques to the predator-prey pursuit problem. We first demonstrate that Kanerva Coding applied within a reinforcement learner does not give good results. We then describe our new adaptive Kanerva-based function approximation algorithm, based on prototype deletion and generation. We show that probabilistic prototype deletion with random prototype generation increases the fraction of test instances that are solved from 45% to 90%, and that prototype splitting increases that fraction to 94%. We also show that optimizing prototypes reduces the number of prototypes, and therefore the number of features, needed to achieve a 90% solution rate by up to 87%. These results demonstrate that our approach can dramatically improve the quality of the results obtained and reduce the number of prototypes required. We conclude that adaptive prototype optimization can greatly improve a Kanerva-based reinforcement learner's ability to solve large-scale multi-agent problems.