Technical Note: \cal Q-Learning
Machine Learning
On complexity as bounded rationality (extended abstract)
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
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 learning using a variable learning rate
Artificial Intelligence
On No-Regret Learning, Fictitious Play, and Nash Equilibrium
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile Robots
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Implicit Negotiation in Repeated Games
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
A portfolio approach to algorithm select
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Accident or intention: that is the question (in the Noisy Iterated Prisoner's Dilemma)
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Learning against multiple opponents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
Perspectives on multiagent learning
Artificial Intelligence
Multiagent learning in adaptive dynamic systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Competition and Coordination in Stochastic Games
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Online Multiagent Learning against Memory Bounded Adversaries
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
On the use of memory and resources in minority games
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
An information-theoretic analysis of memory bounds in a distributed resource allocation mechanism
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Effective learning in the presence of adaptive counterparts
Journal of Algorithms
Online model learning in adversarial Markov decision processes
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Sequential targeted optimality as a new criterion for teaching and following in repeated games
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Leading ad hoc agents in joint action settings with multiple teammates
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Cooperating with a markovian ad hoc teammate
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Teaching and leading an ad hoc teammate: Collaboration without pre-coordination
Artificial Intelligence
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Multiagent learning in the presence of memory-bounded agents
Autonomous Agents and Multi-Agent Systems
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Recently, a number of authors have proposed criteria for evaluating learning algorithms in multiagent systems. While well-justified, each of these has generally given little attention to one of the main challenges of a multi-agent setting: the capability of the other agents to adapt and learn as well. We propose extending existing criteria to apply to a class of adaptive opponents with bounded memory. We then show an algorithm that provably achieves an o-best response against this richer class of opponents while simultaneously guaranteeing a minimum payoff against any opponent and performing well in self-play. This new algorithm also demonstrates strong performance in empirical tests against a variety of opponents in a wide range of environments.