Types of functions | Spectral theory

Proto-value function

In applied mathematics, proto-value functions (PVFs) are automatically learned basis functions that are useful in approximating task-specific value functions, providing a compact representation of the powers of transition matrices. They provide a novel framework for solving the credit assignment problem. The framework introduces a novel approach to solving Markov decision processes (MDP) and reinforcement learning problems, using multiscale spectral and manifold learning methods. Proto-value functions are generated by spectral analysis of a graph, using the graph Laplacian. Proto-value functions were first introduced in the context of reinforcement learning by Sridhar Mahadevan in his paper, Proto-Value Functions: Developmental Reinforcement Learning at ICML 2005. (Wikipedia).

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Spectral graph theory | Eigenfunction | Adjacency matrix | Function approximation | Markov decision process | Random walk | Laplacian matrix | Reinforcement learning | Self-adjoint operator | Basis function | Degree matrix | Applied mathematics