# FRAC-Q-Learning: A Reinforcement Learning with Boredom Avoidance Processes for Social Robots

arXiv: https://arxiv.org/abs/2311.15327 · HTML: https://mlsanity.com/paper/2311.15327/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 26 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Reinforcement Learning Approaches in Social Robotics](https://mlsanity.com/paper/2009.09689/) (2020)

## Abstract (arXiv)

The reinforcement learning algorithms have often been applied to social robots. However, most reinforcement learning algorithms were not optimized for the use of social robots, and consequently they may bore users. We proposed a new reinforcement learning method specialized for the social robot, the FRAC-Q-learning, that can avoid user boredom. The proposed algorithm consists of a forgetting process in addition to randomizing and categorizing processes. This study evaluated interest and boredom hardness scores of the FRAC-Q-learning by a comparison with the traditional Q-learning. The FRAC-Q-learning showed significantly higher trend of interest score, and indicated significantly harder to bore users compared to the traditional Q-learning. Therefore, the FRAC-Q-learning can contribute to develop a social robot that will not bore users. The proposed algorithm has a potential to apply for Web-based communication and educational systems. This paper presents the entire process, detailed implementation and a detailed evaluation method of the of the FRAC-Q-learning for the first time.