Proto-Value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes¶
Proto-Value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes.
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Domain-specific¶
- Proto-value function
- … or sampled state space, transition graph and weights, Laplacian or diffusion operator, eigenproblem, basis selection, task independence, value approximation and coverage error are explicit. to An applied instance preserves the same invariant under a changed scale, notation, jurisdiction, dataset, or implementation..
This sourceFor example, states close in Euclidean distance (such as states on opposite sides of a wall) may be far apart in manifold space. Previous approaches to this nonlinearity problem lacked a broad theoretical framework, and consequently have only been explored in the context of discrete MDPs. Proto-value functions arise from reformulating the problem of value function approximation as real-valued function approximation on a graph or manifold. This results in broader applicability of the learned bases and enables a new class of learning algorithms, which learn representations and policies at the same time. Mahadevan, S. and Maggiono, M., Proto-Value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes, University of Massachusetts, Department of Computer Science Technical Report TR-2006-35, 2006.
- … or sampled state space, transition graph and weights, Laplacian or diffusion operator, eigenproblem, basis selection, task independence, value approximation and coverage error are explicit. to An applied instance preserves the same invariant under a changed scale, notation, jurisdiction, dataset, or implementation..
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