Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Cooperative Q Learning Based on Blackboard Architecture
CISW '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security Workshops
Interaction Models for Multiagent Reinforcement Learning
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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
Expertness based cooperative Q-learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Study on Expertise of Agents and Its Effects on Cooperative -Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Towards student/teacher learning in sequential decision tasks
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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Past research on multi-agent simulation with cooperative reinforcement learning (RL) focuses on developing sharing strategies that are adopted and used by all agents in the environment. In this paper, we target situations where this assumption of a single sharing strategy that is employed by all agents is not valid. We seek to address how agents with no predetermined sharing partners can exploit groups of cooperatively learning agents to improve learning performance when compared to Independent learning. Specifically, we propose 3 intra-agent methods that do not assume a reciprocating sharing relationship and leverage the pre-existing agent interface associated with Q-Learning to expedite learning.