Convergent activation dynamics in continuous time networks
Neural Networks
Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Technical Note: \cal Q-Learning
Machine Learning
Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
Stochastic approximation with two time scales
Systems & Control Letters
The O.D. E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
SIAM Journal on Control and Optimization
Actor-Critic--Type Learning Algorithms for Markov Decision Processes
SIAM Journal on Control and Optimization
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
An analysis of reinforcement learning with function approximation
Proceedings of the 25th international conference on Machine learning
Q-learning with linear function approximation
COLT'07 Proceedings of the 20th annual conference on Learning theory
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A simulation-based algorithm for learning good policies for a discrete-time stochastic control process with unknown transition law is analyzed when the state and action spaces are compact subsets of Euclidean spaces. This extends the Q-learning scheme of discrete state/action problems along the lines of Baker [4]. Almost sure convergence is proved under suitable conditions.