Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Near-Optimal Reinforcement Learning in Polynominal Time
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Dynamic non-Bayesian decision making
Journal of Artificial Intelligence Research
Fast planning in stochastic games
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
We present a new algorithm for polynomial time learning of near optimal behavior in stochastic games. This algorithm incorporates and integrates important recent results of Kearns and Singh [1998] in reinforcement learning and of Monderer and Tennenholtz [1997] in repeated games. In stochastic games we face an exploration vs. exploitation dilemma more complex than in Markov decision processes. Namely, given information about particular parts of a game matrix, how much effort should the agent invest in learning its unknown parts. We explain and address these issues within the class of single controller stochastic games. This solution can be extended to stochastic games in general.