Automatic programming of behavior-based robots using reinforcement learning
Artificial Intelligence
Technical Note: \cal Q-Learning
Machine Learning
Reinforcement learning for robots using neural networks
Reinforcement learning for robots using neural networks
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
SIAM Journal on Control and Optimization
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Actor-Critics constitute an important class of reinforcement learning algorithms that can deal with continuous actions and states in an easy and natural way. In their original, sequential form, these algorithms are usually to slow to be applicable to real-life problems. However, they can be augmented by the technique of experience replay to obtain a satisfactory of learning without degrading their convergence properties. In this paper experimental results are presented that show that the combination of experience replay and Actor-Critics yields very fast learning algorithms that achieve successful policies for nontrivial control tasks in considerably short time. Namely, a policy for a model of 6-degree-of-freedom walking robot is obtained after 4 hours of the robot's time.