reinforcement-learning

Reinforcement learning (RL) is becoming more prevalent in practical domains with human implications, raising ethical questions. …

Reinforcement learning (RL) is becoming more prevalent in practical domains with human implications, raising ethical questions. …

Paper published in the Journal of Open-Source Software, alongside the source code for our ethical-smart-grid simulator. This simulator focuses on ethical behaviours within a Smart Grid, and is based on Gym (Reinforcement Learning).

Journal paper published at the French Artificial Intelligence Open Journal (Revue Ouverte d’Intelligence Artificielle). This works extends previous works, especially the conference paper published at JFSMA 2021.

Preprint describing two Reinforcement Learning algorithms (Q-SOM and Q-DSOM) I have developped. They focus on continuous and multi-dimensional observations and actions, and adaptation to changes in the environment.

Paper presented at the Autonomous Agents and Multiagent Systems conference. It presents the AJAR framework, which uses argumentation-based judging agents to provide rewards for Reinforcement Learning agents, according to one or several moral values. This “judgment of ethics” is used to nudge the learning agents towards an “ethical behavior”, that is, a behavior aligned with the given moral values.

This paper presents how Artificial Intelligence could be used to help humans in their ethical decision-making tasks.

Smart Grid simulator for Reinforcement Learning focusing on ethical behaviours.

Paper on the ability to use a symbolic reasoning approach to judge neural learning agents, in order to reward them appropriately with respect to their ’ethical’ behavior, combining both approaches in a Hybrid method.

Paper presented at the AI, Ethics, and Society conference. It presents a novel, hybrid, method to learn “ethical behaviors” by combining symbolic judgments with a Reinforcement Learning algorithm.