Introduction to the theory of neural computation
Introduction to the theory of neural computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Least-Squares Temporal Difference Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
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Reinforcement Learning (RL) approaches are, very often, rendered useless by the statistics of the required sampling process. This paper shows how very fastRL is essentially made possible by abandoning the state feedback during training episodes. The resulting new method, feed-forward learning(FF learning), employs a return estimator for pairs of a state and a feed-forward policy'sparameter vector. FF learning is particularly suitable for the learning of controllers, e.g. for robotics applications, and yields learning rates unprecedented in the RL context.This paper introduces the method formally and proves a lower bound on its performance. Practical results are provided from applying FF learning to several scenarios based on the collision avoidance behavior of a mobile robot.