Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
We present a method for creating a geometry-dependent basis for precomputed radiance transfer. Unlike previous PRT bases, ours is derived from principal component analysis of the ...
Derek Nowrouzezahrai, Patricio D. Simari, Evangelo...
A novel stochastic searching scheme based on the Monte Carlo optimization is presented for polygonal approximation (PA) problem. We propose to combine the split-and-merge based lo...
Abstract--Reconstruction algorithms for fluorescence tomography have to address two crucial issues : (i) the ill-posedness of the reconstruction problem, (ii) the large scale of nu...
Rich representations in reinforcement learning have been studied for the purpose of enabling generalization and making learning feasible in large state spaces. We introduce Object...