Year: 2014
Type: preprint
Abstract: In Reinforcement Learning (RL), it is common to use optimistic initialization of value functions to encourage exploration. However, such an approach generally depends on the domain, viz., the scale of the rewards must be known, and the feature representation must have a constant norm. We present a simple approach that performs optimistic initialization with less dependence on the domain.
Source: arXiv (Cornell University)
Authors: Machado, Marlos C., Srinivasan, Sriram, Bowling, Michael
Institutions: Universidade Federal de Minas Gerais, University of Alberta
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W164946830