Tagged #MARL

A short-story about why I decided to do a PhD on the subject of explainable multi-agent reinforcement learning. I detail how I weighted this decision and how I created my proposal. I also try to depict what I plan to do for making the best of my PhD.

By addressing Multi-Agent Deep Reinforcement Learning interpretability, we propose directions aiming to advance active topics such as team identification, swarm coordination and sample efficiency. This paper advocates for direct interpretability, generating post hoc explanations directly from trained models, offering scalable insights into agents' behaviour, emergent phenomena, and biases without altering models' architectures.