Linear least-squares algorithms for temporal difference learning
Machine Learning - Special issue on reinforcement 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
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
Basis Expansion in Natural Actor Critic Methods
Recent Advances in Reinforcement Learning
Fast gradient-descent methods for temporal-difference learning with linear function approximation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Cross-Entropy Optimization of Control Policies With Adaptive Basis Functions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We consider the problem of reinforcement learning using function approximation, where the approximating basis can change dynamically while interacting with the environment. A motivation for such an approach is maximizing the value function fitness to the problem faced. Three errors are considered: approximation square error, Bellman residual, and projected Bellman residual. Algorithms under the actorcritic framework are presented, and shown to converge. The advantage of such an adaptive basis is demonstrated in simulations.