Learning automata: an introduction
Learning automata: an introduction
Technical Note: \cal Q-Learning
Machine Learning
Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
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
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Exploring selfish reinforcement learning in repeated games with stochastic rewards
Autonomous Agents and Multi-Agent Systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Sequential optimality and coordination in multiagent systems
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Learning automata as a basis for multi agent reinforcement learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
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Hierarchical learning automata are shown to be an excellent tool for solving multi-stage games. However, most updating schemes used by hierarchical automata expect the multi-stage game to reach an absorbing state at which point the automata are updated in a Monte Carlo way. As such, the approach is infeasible for large multi-stage games (and even for problems with an infinite horizon) and the convergence process is slow. In this paper we propose an algorithm where the rewards don't have to travel all the way up to the top of the hierarchy and in which there is no need for explicit end-stages.