13 search results - page 1 / 3 » Learning state-action basis functions for hierarchical MDPs |
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Voted
ICML
2007
IEEE
15 years 10 months ago
2007
IEEE
This paper introduces a new approach to actionvalue function approximation by learning basis functions from a spectral decomposition of the state-action manifold. This paper exten...
ICML
2007
IEEE
15 years 10 months ago
2007
IEEE
Basis functions derived from an undirected graph connecting nearby samples from a Markov decision process (MDP) have proven useful for approximating value functions. The success o...
AAAI
2006
14 years 11 months ago
2006
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
ATAL
2010
Springer
14 years 10 months ago
2010
Springer
This paper introduces an approach to automatic basis function construction for Hierarchical Reinforcement Learning (HRL) tasks. We describe some considerations that arise when con...
NCI
2004
14 years 11 months ago
2004
This paper describes a method for hierarchical reinforcement learning in which high-level policies automatically discover subgoals, and low-level policies learn to specialize for ...