Technical Note: \cal Q-Learning
Machine Learning
Learning to coordinate without sharing information
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
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
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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
Context-specific multiagent coordination and planning with factored MDPs
Eighteenth national conference on Artificial intelligence
Planning under uncertainty in complex structured environments
Planning under uncertainty in complex structured environments
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
Real World Multi-agent Systems: Information Sharing, Coordination and Planning
Logic, Language, and Computation
Solving multiagent assignment Markov decision processes
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Learning of coordination: exploiting sparse interactions in multiagent systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Partial Local FriendQ Multiagent Learning: Application to Team Automobile Coordination Problem
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Multi-robot cooperative pursuit based on association rule data mining
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Learning multi-agent state space representations
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Generalized learning automata for multi-agent reinforcement learning
AI Communications - European Workshop on Multi-Agent Systems (EUMAS) 2009
Decentralized MDPs with sparse interactions
Artificial Intelligence
Patching approximate solutions in reinforcement learning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Partial local friendq multiagent learning: application to team automobile coordination problem
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Solving sparse delayed coordination problems in multi-agent reinforcement learning
ALA'11 Proceedings of the 11th international conference on Adaptive and Learning Agents
Coordinated learning for loosely coupled agents with sparse interactions
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Hi-index | 0.00 |
Learning in multiagent systems suffers from the fact that both the state and the action space scale exponentially with the number of agents. In this paper we are interested in using Q-learning to learn the coordinated actions of a group of cooperative agents, using a sparse representation of the joint state-action space of the agents. We first examine a compact representation in which the agents need to explicitly coordinate their actions only in a predefined set of states. Next, we use a coordination-graph approach in which we represent the Q-values by value rules that specify the coordination dependencies of the agents at particular states. We show how Q-learning can be efficiently applied to learn a coordinated policy for the agents in the above framework. We demonstrate the proposed method on the predator-prey domain, and we compare it with other related multiagent Q-learning methods.