Sep 11, 2018 · With the help of nested optimization decomposition, ℓ 1 -RC solves a series of standard optimization problems and avoids minimizing mean squares ...
Nested optimization decomposition can change one unsolvable optimization problem into a series of standard subproblems. As a combination of RLS-based TD with ...
This work considers the tasks of feature selection and policy evaluation based on linear value function approximation in reinforcement learning problems and ...
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We propose two new algorithms for the sparse re- inforcement learning problem based on different formulations. The first algorithm is an off-line method ...
Sep 27, 2022 · Maximum entropy (ME) algorithms have been broadly studied towards the learning of rewards and obtaining of optimal policies for inverse ...
Maximum entropy (ME) algorithms have been broadly studied towards the learning of rewards and obtaining of optimal policies for inverse reinforcement ...
In this paper we approach the sparse reinforcement learning problem with a new constrained formulation that explicitly controls the projected Bellman residual ( ...
Missing: Nested | Show results with:Nested
Abstract. Sparse coding consists in representing signals as sparse linear combinations of atoms selected from a dictionary. We consider an extension of this ...
Missing: Reinforcement | Show results with:Reinforcement