Technical Note: \cal Q-Learning
Machine Learning
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
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Non-Monotonic-Offers Bargaining Protocol
Autonomous Agents and Multi-Agent Systems
Combinatorial Auctions
Designing a Simulation Middleware for FIPA Multiagent Systems
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Frequency adjusted multi-agent Q-learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Monotonic mixing of decision strategies for agent-based bargaining
MATES'11 Proceedings of the 9th German conference on Multiagent system technologies
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Iterated negotiations are a well-established method for coordinating distributed activities in multiagent systems. However, if several of these take place concurrently, the participants' activities can mutually influence each other. In order to cope with the problem of interrelated interaction outcomes in partially observable environments, we apply distributed reinforcement learning to concurrent many-object negotiations. To this end, we discuss iterated negotiations from the perspective of repeated games, specify the agents' learning behavior, and introduce decentral decision-making criteria for terminating a negotiation. Furthermore, we empirically evaluate the approach in a multiagent resource allocation scenario. The results show that our method enables the agents to successfully learn mutual best response behaviors which approximate Nash equilibrium allocations. Additionally, the learning constrains the required interaction effort for attaining these results.