Technical Note: \cal Q-Learning
Machine Learning
Multiagent learning using a variable learning rate
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Designing Social Cognition Models for Multi-Agent Systems through Simulating Primate Societies
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Multi-agent reinforcement learning and chimpanzee hunting
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Value-function reinforcement learning in Markov games
Cognitive Systems Research
Multi-agent reinforcement learning and chimpanzee hunting
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
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Multi-agent systems are becoming more popular in a variety of problem domains that benefit from increased parallelism, system robustness, and scalability, ranging from search and rescue to investment management. Multi-agent systems analysis studies how multiple agents coordinate with each other to maximize some team goal or individual best reward. Coordination achieved through learning provides a great advantage over modeling methods, especially when tasks become very complex and environments more dynamic. Because social primates such as chimpanzees are a highly successful multi-agent system that uses learning to adapt flexibly to changing social and environmental conditions, we are attempting to simulate their social cognition and behavior. The paper presents a foraging task to study how multiple agents can use reinforcement learning to coordinate as a group under social constraints, while also trying to maximize their own reward. Each distributed, heterogenous agent uses the WoLFPHC algorithm, and with no communication, the agents learn to select the best foraging patch based on the behavior of others through the "Win or Learn Fast" heuristic. The simulation results demonstrate that the agents can perform in a manner similar to the natural social behavior of chimpanzees, and show that we have a working model system for studying more complex chimpanzee social behavior in the future.