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
Friend-or-Foe Q-learning in General-Sum Games
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms
Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms
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
Multi-goal Q-learning of cooperative teams
Expert Systems with Applications: An International Journal
The evolution of rules for conflicts resolution in self-organizing teams
Expert Systems with Applications: An International Journal
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A reinforcement learning algorithm OP-Qfor multi-agent systems based on Hurwicz's optimistic-pessimistic criterion which allows to embed preliminary knowledge on the degree of environment friendliness is proposed. The proof of its convergence to stationary policy is given. Thorough testing of the developed algorithm against well-known reinforcement learning algorithms has shown that OP-Qcan function on the level of its opponents.