# Adaptive reinforcement learning of multi-agent ethically-aligned behaviours: the QSOM and QDSOM algorithms

arXiv: https://arxiv.org/abs/2307.00552 · HTML: https://mlsanity.com/paper/2307.00552/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 2 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [Building Ethics into Artificial Intelligence](https://mlsanity.com/paper/1812.02953/) (2018)
- [A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents](https://mlsanity.com/paper/1712.04172/) (2017)
- [Landscape of Machine Implemented Ethics](https://mlsanity.com/paper/2009.00335/) (2020)

## Abstract (arXiv)

The numerous deployed Artificial Intelligence systems need to be aligned with our ethical considerations. However, such ethical considerations might change as time passes: our society is not fixed, and our social mores evolve. This makes it difficult for these AI systems; in the Machine Ethics field especially, it has remained an under-studied challenge. In this paper, we present two algorithms, named QSOM and QDSOM, which are able to adapt to changes in the environment, and especially in the reward function, which represents the ethical considerations that we want these systems to be aligned with. They associate the well-known Q-Table to (Dynamic) Self-Organizing Maps to handle the continuous and multi-dimensional state and action spaces. We evaluate them on a use-case of multi-agent energy repartition within a small Smart Grid neighborhood, and prove their ability to adapt, and their higher performance compared to baseline Reinforcement Learning algorithms.