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
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Achieving goals in decentralized POMDPs
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Scaling up optimal heuristic search in Dec-POMDPs via incremental expansion
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Concurrent reinforcement learning as a rehearsal for decentralized planning under uncertainty
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Decentralized partially-observable Markov decision processes (Dec-POMDPs) are a powerful tool for modeling multi-agent planning and decision-making under uncertainty. Prevalent Dec-POMDP solution techniques require centralized computation given full knowledge of the underlying model. But in real world scenarios, model parameters may not be known a priori, or may be difficult to specify. We propose to address these limitations with distributed reinforcement learning (RL).