Competitive Markov decision processes
Competitive Markov decision processes
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 Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Sequential Optimality and Coordination in Multiagent Systems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Reinforcement learning of coordination in cooperative multi-agent systems
Eighteenth national conference on Artificial intelligence
Rational and convergent learning in stochastic games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Model based Bayesian exploration
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Bayesian Reinforcement Learning for Coalition Formation under Uncertainty
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Multi-Agent Patrolling with Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Coordinating Multiple Agents via Reinforcement Learning
Autonomous Agents and Multi-Agent Systems
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Learning against multiple opponents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Coalition formation mechanism in multi-agent systems based on genetic algorithms
Applied Soft Computing
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
A layered approach to learning coordination knowledge in multiagent environments
Applied Intelligence
Agent learning in the multi-agent contracting system [MACS]
Decision Support Systems
Knowledge propagation in a distributed omnidirectional vision system
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Marco Somalvico Memorial Issue
Sequential decision making with untrustworthy service providers
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Sequential decision making in repeated coalition formation under uncertainty
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Emerging coordination in infinite team Markov games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Towards Trust-Based Acquisition of Unverifiable Information
CIA '08 Proceedings of the 12th international workshop on Cooperative Information Agents XII
BROA: A Bayesian Robotic Agents Architecture
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Combining Cognitive with Computational Trust Reasoning
Trust in Agent Societies
A Team CGA Learning Method TCCLA
CAR '09 Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics
Dynamic information source selection for intrusion detection systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
QUICR-learning for multi-agent coordination
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Anytime Self-play Learning to Satisfy Functional Optimality Criteria
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
Cooperative Sign Language Tutoring: A Multiagent Approach
ESAW '09 Proceedings of the 10th International Workshop on Engineering Societies in the Agents World X
Cooperative multi-robot reinforcement learning: a framework in hybrid state space
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Multi-agent learning: how to interact to improve collective results
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Learning and Meta-Learning for Coordination of Autonomous Unmanned VehiclesA Preliminary Analysis
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Sequentially optimal repeated coalition formation under uncertainty
Autonomous Agents and Multi-Agent Systems
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
You are what you consume: a bayesian method for personalized recommendations
Proceedings of the 7th ACM conference on Recommender systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 0.00 |
Much emphasis in multiagent reinforcement learning (MARL) research is placed on ensuring that MARL algorithms (eventually) converge to desirable equilibria. As in standard reinforcement learning, convergence generally requires sufficient exploration of strategy space. However, exploration often comes at a price in the form of penalties or foregone opportunities. In multiagent settings, the problem is exacerbated by the need for agents to "coordinate" their policies on equilibria. We propose a Bayesian model for optimal exploration in MARL problems that allows these exploration costs to be weighed against their expected benefits using the notion of value of information. Unlike standard RL models, this model requires reasoning about how one's actions will influence the behavior of other agents. We develop tractable approximations to optimal Bayesian exploration, and report on experiments illustrating the benefits of this approach in identical interest games.