Pricing computer services: queueing effects
Communications of the ACM
Technical Note: \cal Q-Learning
Machine Learning
Stochastic Shortest Path Games
SIAM Journal on Control and Optimization
Actor-Critic--Type Learning Algorithms for Markov Decision Processes
SIAM Journal on Control and Optimization
Convergence of Gradient Dynamics with a Variable Learning Rate
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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
The possible and the impossible in multi-agent learning
Artificial Intelligence
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A multiagent reinforcement learning algorithm with non-linear dynamics
Journal of Artificial Intelligence Research
Nash convergence of gradient dynamics in general-sum games
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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We consider state-dependent pricing in a two-player service market stochastic game where state of the game and its transition dynamics are modeled using a semi-Markovian queue. We propose a multi-time scale actor-critic based reinforcement algorithm for multi-agent learning under self-play and provide experimental results on Nash convergence.