Another hybrid conjugate gradient algorithm is subject to analysis. The parameter k is computed as a convex combination of HS k (Hestenes-Stiefel) and DY k (Dai-Yuan) algorithms, i...
Policy search is a method for approximately solving an optimal control problem by performing a parametric optimization search in a given class of parameterized policies. In order ...
We introduce an algorithm that learns gradients from samples in the supervised learning framework. An error analysis is given for the convergence of the gradient estimated by the ...
There exist a number of reinforcement learning algorithms which learn by climbing the gradient of expected reward. Their long-run convergence has been proved, even in partially ob...
We investigate the problem of non-covariant behavior of policy gradient reinforcement learning algorithms. The policy gradient approach is amenable to analysis by information geom...
Policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation (Baxter and Bartlett, 2001...
Evan Greensmith, Peter L. Bartlett, Jonathan Baxte...
To apply empty space skipping in texture-based volume rendering, we partition the texture space with a box-growing algorithm. Each sub-texture comprises of neighboring voxels with...
We present four new reinforcement learning algorithms based on actor-critic and natural-gradient ideas, and provide their convergence proofs. Actor-critic reinforcement learning m...
Shalabh Bhatnagar, Richard S. Sutton, Mohammad Gha...
We analyze the problem of reconstructing a 2D function that approximates a set of desired gradients and a data term. The combined data and gradient terms enable operations like mod...
Pravin Bhat, Brian Curless, Michael F. Cohen, C. L...
The use of gradients in text images is nowadays quite frequent. Existing segmentation methods encounter serious problems when it comes to modern text images where gradients might ...