Adaptive reinforcement learning of multi-agent ethically-aligned behaviours: the QSOM and QDSOM algorithms
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.
Code (0)
등록된 구현이 없습니다.
Tasks
EthicsSimilar Papers 제목 키워드 기반
Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach
Participatory budgeting is a method of collectively understanding and addressing spending priorities where citizens vote on how a budget is spent, it is regularly run to improve the fairness of the distribution of public…
Multi-agent Reinforcement LearningDecision MakingMulti-Agent LLMs as Ethics Advocates for AI-Based Systems
Incorporating ethics into the requirement elicitation process is essential for creating ethically aligned systems. Although eliciting manual ethics requirements is effective, it requires diverse input from multiple stake…
A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents
This paper proposes a low-cost, easily realizable strategy to equip a reinforcement learning (RL) agent the capability of behaving ethically. Our model allows the designers of RL agents to solely focus on the task to ach…
Ethicsreinforcement-learningReinforcement LearningReinforcement Learning (RL)Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being
Affective computing has made significant strides in emotion recognition and generation, yet current approaches mainly focus on short-term pattern recognition and lack a comprehensive framework to guide affective agents t…
Emotion RecognitionMeta Reinforcement LearningBeyond Arrow's Impossibility: Fairness as an Emergent Property of Multi-Agent Collaboration
Fairness in language models is typically studied as a property of a single, centrally optimized model. As large language models become increasingly agentic, we propose that fairness emerges through interaction and exchan…