Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Interaction Models for Multiagent Reinforcement Learning
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
Multi-Agent cooperative reinforcement learning in 3d virtual world
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Multi-agent reinforcement learning for simulating pedestrian navigation
ALA'11 Proceedings of the 11th international conference on Adaptive and Learning Agents
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Training agents in a virtual crowd to achieve a task can be accomplished by allowing the agents to learn by trial-and-error and by sharing information with other agents. Since sharing enables agents to potentially reach optimal behavior more quickly, what type of sharing is best to use to achieve the quickest learning times? This paper categorizes sharing into three categories: realistic, unrealistic, and no sharing. Realistic sharing is defined as sharing that takes place amongst agents within close proximity and unrealistic sharing allows agents to share regardless of physical location. This paper demonstrates that all sharing methods converge to similar policies and that the differences between the methods are determined by analyzing the learning rates, communication frequencies, and total run times. Results show that the unrealistic-centralized sharing method --- where agents update a common learning module --- is the most effective of the sharing methods tested.