Learning automata: an introduction
Learning automata: an introduction
Introduction to Reinforcement Learning
Introduction to Reinforcement 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
What evolutionary game theory tells us about multiagent learning
Artificial Intelligence
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
The Journal of Machine Learning Research
Switching dynamics of multi-agent learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Qualitative simulation of genetic regulatory networks: method and application
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Networks of learning automata and limiting games
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
ICEC'10 Proceedings of the 9th international conference on Entertainment computing
Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research
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This paper extends the link between evolutionary game theory and multi-agent reinforcement learning to multistate games. In previous work, we introduced piecewise replicator dynamics, a combination of replicators and piecewise models to account for multi-state problems. We formalize this promising proof of concept and provide definitions for the notion of average reward games, pure equilibrium cells and finally, piecewise replicator dynamics. These definitions are general in the number of agents and states. Results show that piecewise replicator dynamics qualitatively approximate multi-agent reinforcement learning in stochastic games.