Technical Note: \cal Q-Learning
Machine Learning
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Optimizing information exchange in cooperative multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Decentralized Markov Decision Processes with Event-Driven Interactions
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Coordination through Mutual Notification in Cooperative Multiagent Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Coordinated exploration in multi-agent reinforcement learning: an application to load-balancing
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
Shaping multi-agent systems with gradient reinforcement learning
Autonomous Agents and Multi-Agent Systems
Reinforcement learning for DEC-MDPs with changing action sets and partially ordered dependencies
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Learning to Coordinate Efficiently: a model-based approach
Journal of Artificial Intelligence Research
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Sequential optimality and coordination in multiagent systems
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
ECML'05 Proceedings of the 16th European conference on Machine Learning
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DEC-MDPs with changing action sets and partially ordered transition dependencies have recently been suggested as a sub-class of general DEC-MDPs that features provably lower complexity. In this paper, we investigate the usability of a coordinated batch-mode reinforcement learning algorithm for this class of distributed problems. Our agents acquire their local policies independent of the other agents by repeated interaction with the DEC-MDP and concurrent evolvement of their policies, where the learning approach employed builds upon a specialized variant of a neural fitted Q iteration algorithm, enhanced for use in multi-agent settings. We applied our learning approach to various scheduling benchmark problems and obtained encouraging results that show that problems of current standards of difficulty can very well approximately, and in some cases optimally be solved.