Now showing items 1-2 of 2
Scaling Reinforcement Learning Paradigms for Motor Control
Reinforcement learning offers a general framework to explain reward related learning in artificial and biological motor control. However, current reinforcement learning methods rarely scale to high dimensional movement systems ...
Geodesic Gaussian kernels for value function approximation
The least-squares policy iteration approach works efficiently in value function approximation, given appropriate basis functions. Because of its smoothness, the Gaussian kernel is a popular and useful choice as a basis ...