Technical Note: \cal Q-Learning
Machine Learning
Multiagent learning using a variable learning rate
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Convergence Problems of General-Sum Multiagent Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A selection-mutation model for q-learning in multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games
Autonomous Agents and Multi-Agent Systems
A novel method for automatic strategy acquisition in N-player non-zero-sum games
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
What evolutionary game theory tells us about multiagent learning
Artificial Intelligence
Modelling the dynamics of multiagent Q-learning with ε-greedy exploration
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
An evolutionary model of multi-agent learning with a varying exploration rate
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Teamwork in self-organized robot colonies
IEEE Transactions on Evolutionary Computation
Empirical and theoretical support for lenient learning
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
A common gradient in multi-agent reinforcement learning
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Distributed learning of best response behaviors in concurrent iterated many-object negotiations
MATES'12 Proceedings of the 10th German conference on Multiagent System Technologies
Evolutionary dynamics of ant colony optimization
MATES'12 Proceedings of the 10th German conference on Multiagent System Technologies
Multi-agent learning and the reinforcement gradient
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
On measuring social intelligence: experiments on competition and cooperation
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
Addressing the policy-bias of q-learning by repeating updates
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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Multi-agent learning is a crucial method to control or find solutions for systems, in which more than one entity needs to be adaptive. In today's interconnected world, such systems are ubiquitous in many domains, including auctions in economics, swarm robotics in computer science, and politics in social sciences. Multi-agent learning is inherently more complex than single-agent learning and has a relatively thin theoretical framework supporting it. Recently, multi-agent learning dynamics have been linked to evolutionary game theory, allowing the interpretation of learning as an evolution of competing policies in the mind of the learning agents. The dynamical system from evolutionary game theory that has been linked to Q-learning predicts the expected behavior of the learning agents. Closer analysis however allows for two interesting observations: the predicted behavior is not always the same as the actual behavior, and in case of deviation, the predicted behavior is more desirable. This discrepancy is elucidated in this article, and based on these new insights Frequency Adjusted Q- (FAQ-) learning is proposed. This variation of Q-learning perfectly adheres to the predictions of the evolutionary model for an arbitrarily large part of the policy space. In addition to the theoretical discussion, experiments in the three classes of two-agent two-action games illustrate the superiority of FAQ-learning.