Adaptive reinforcement learning of multi-agent ethically-aligned behaviours: the QSOM and QDSOM algorithms
This addresses the problem of ethical alignment in AI for machine ethics, though it appears incremental as it builds on existing methods like Q-Tables and Self-Organizing Maps.
The paper tackles the challenge of aligning AI systems with evolving ethical considerations by introducing QSOM and QDSOM algorithms, which adapt to changes in reward functions and achieve higher performance compared to baseline reinforcement learning algorithms in a multi-agent energy repartition scenario.
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.