The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Coordinated Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Tree consistency and bounds on the performance of the max-product algorithm and its generalizations
Statistics and Computing
Relational temporal difference learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
Online learning and exploiting relational models in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Distributed Coordination Guidance in Multi-agent Reinforcement Learning
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Multi-agent relational reinforcement learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
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
Coordination guided reinforcement learning
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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Relational representations have great potential for rapidly generalizing learned knowledge in large Markov decision processes such as multi-agent problems. In this work, we introduce relational temporal difference learning for the distributed case where the communication links among agents are dynamic. Thus no critical components of the system should reside in any one agent. Relational generalization among agents' learning is achieved through the use of partially bound relational features and a message passing scheme. We further describe how the proposed concepts can be applied to distributed reinforcement learning methods that use value functions. Experiments were conducted on soccer and real-time strategy game domains with dynamic communication. Results show that our methods improve goal achievement in online learning with a greatly decreased number of parameters to learn when compared with existing distributed learning methods.