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
Learning in multiagent systems
Multiagent systems
Multiagent learning using a variable learning rate
Artificial Intelligence
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
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
Nash Convergence of Gradient Dynamics in General-Sum Games
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
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
Efficient learning equilibrium
Artificial Intelligence
Hedged learning: regret-minimization with learning experts
ICML '05 Proceedings of the 22nd international conference on Machine learning
Efficient no-regret multiagent learning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A fuzzy constraint-based agent negotiation with opponent learning
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
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
Proceedings of the 13th ACM Conference on Electronic Commerce
When speed matters in learning against adversarial opponents
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Cooperating with a markovian ad hoc teammate
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Multiagent learning in the presence of memory-bounded agents
Autonomous Agents and Multi-Agent Systems
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We offer a new formal criterion for agent-centric learning in multi-agent systems, that is, learning that maximizes one's rewards in the presence of other agents who might also be learning (using the same or other learning algorithms). This new criterion takes in as a parameter the class of opponents. We then provide a modular approach for achieving effective agent-centric learning; the approach consists of a number of basic algorithmic building blocks, which can be instantiated and composed differently depending on the environment setting (for example, 2- versus n-player games) as well as the target class of opponents. We then provide several specific instances of the approach: an algorithm for stationary opponents, and two algorithms for adaptive opponents with bounded memory, one algorithm for the n-player case and another optimized for the 2-player case. We prove our algorithms correct with respect to the formal criterion, and furthermore show the algorithms to be experimentally effective via comprehensive computer testing.