Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
Specification faithfulness in networks with rational nodes
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
Efficient learning equilibrium
Artificial Intelligence
Selfish routing with incomplete information
Proceedings of the seventeenth annual ACM symposium on Parallelism in algorithms and architectures
Resource selection games with unknown number of players
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Learning equilibrium as a generalization of learning to optimize
Artificial Intelligence
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Optimal efficient learning equilibrium: imperfect monitoring in symmetric games
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Learning equilibrium in resource selection games
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Rational and convergent learning in stochastic games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Price of anarchy of network routing games with incomplete information
WINE'05 Proceedings of the First international conference on Internet and Network Economics
SAGT '09 Proceedings of the 2nd International Symposium on Algorithmic Game Theory
Structured coalitions in resource selection games
ACM Transactions on Intelligent Systems and Technology (TIST)
Automated equilibrium analysis of repeated games with private monitoring: a POMDP approach
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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While the class of congestion games has been thoroughly studied in the multi-agent systems literature, settings with incomplete information have received relatively little attention. In this paper we consider a setting in which the cost functions of resources in the congestion game are initially unknown. The agents gather information about these cost functions through repeated interaction, and observations of costs they incur. In this context we consider the following requirement: the agents' algorithms should themselves be in equilibrium, regardless of the actual cost functions and should lead to an efficient outcome. We prove that this requirement is achievable for a broad class of games: repeated symmetric congestion games. Our results are applicable even when agents are somewhat limited in their capacity to monitor the actions of their counterparts, or when they are unable to determine the exact cost they incur from every resource. On the other hand, we show that there exist asymmetric congestion games for which no such equilibrium can be found, not even an inefficient one. Finally we consider equilibria with resistance to the deviation of more than one player and show that these do not exist even in repeated resource selection games.