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
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
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
Reinforcement learning of coordination in cooperative multi-agent systems
Eighteenth national conference on Artificial intelligence
Coordination in multiagent reinforcement learning: a Bayesian approach
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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
Learning against opponents with bounded memory
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
If multi-agent learning is the answer, what is the question?
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
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
Adaptation in games with many co-evolving agents
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
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
| Hi-index | 0.00 |
We address the problem of learning in repeated n-player (as opposed to 2-player) general-sum games, paying particular attention to the rarely addressed situation in which there are a mixture of agents of different types. We propose new criteria requiring that the agents employing a particular learning algorithm work together to achieve a joint best-response against a target class of opponents, while guaranteeing they each achieve at least their individual security-level payoff against any possible set of opponents. We then provide algorithms that provably meet these criteria for two target classes: stationary strategies and adaptive strategies with a bounded memory. We also demonstrate that the algorithm for stationary strategies outperforms existing algorithms in tests spanning a wide variety of repeated games with more than two players.