Document Type
Article
Abstract
In domains such as Human-Robot Collaboration artificial agents must be able to support mutual adaptation and learning. Towards this direction, we use a discrete Soft Actor-Critic agent on a realtime collaborative game with humans. We examine how different allocations of on-line and off-line gradient updates impact the game performance and the total training time. Our results suggest that early allocation of a high number of off-line g/u can accelerate learning while shortening training duration.
Publication Date
7-2-2021
Language
English
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Lygerakis, Fotios; Dagioglou, Maria; and Karkaletsis, Vangelis, "Accelerating Human-Agent Collaborative Reinforcement Learning" (2021). Association of Computing Machinery Open Access Agreement Publications. 50.
https://mavmatrix.uta.edu/utalibraries_acmoapubs/50