Year: 2013
Type: preprint
Abstract: Optimal probabilistic approach in reinforcement learning is computationally infeasible. Its simplification consisting in neglecting difference between true environment and its model estimated using limited number of observations causes exploration vs exploitation problem. Uncertainty can be expressed in terms of a probability distribution over the space of environment models, and this uncertainty can be propagated to the action-value function via Bellman iterations, which are computationally insufficiently efficient though. We consider possibility of directly measuring uncertainty of the action-value function, and analyze sufficiency of this facilitated approach.
Source: arXiv (Cornell University)
Authors: Rodionov, Sergey, Potapov, Alexey, Vinogradov, Yurii
Institution: Laboratoire d’Astrophysique de Marseille
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1483089639