Improving Coordination with Communication in Multi-Agent Reinforcement Learning
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs
Proceedings of the 25th international conference on Machine learning
Learning of coordination: exploiting sparse interactions in multiagent systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning Communication in Interactive Dynamic Influence Diagrams
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
A Hybrid Cooperative Behavior Learning Method for a Rule-Based Shout-Ahead Architecture
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
| Hi-index | 0.00 |
In this paper, we present a reinforcement learning approach for multi-agent communication in order to learn what to communicate, when and to whom. This method is based on introspective agents that can reason about their own actions and data so as to construct appropriate communicative acts. We propose an extension of classical reinforcement learning algorithms for multi-agent communication. We show how communicative acts and memory can help solving non-markovity and a synchronism issues in MAS.