Abstract
In this article, we examine the learning performance of various strategies under different conditions using the Voronoi Q-value element (VQE) based on reward in a single-agent environment, and decide how to act in a certain state. In order to test our hypotheses, we performed computational experiments using several situations such as various angles of rotation of VQEs which are arranged into a lattice structure, various angles of an agent's action rotation that has 4 actions, and a random arrangement of VQEs to correctly evaluate the optimal Q-values for state and action pairs in order to deal with continuous-valued inputs. As a result, the learning performance changes when the angle of VQEs and the angle of action are changed by a specific relative position.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
