3 papers
cs.LG2026
Towards Socially Compliant Navigation in Deep Reinforcement Learning via Proxemics-Based Reward Modeling
Takieddine Soualhi, Jacques Saraydaryan, Laetitia Matignon
Developing effective robot navigation methods in crowded environments is essential for real-world applications. Although recent deep reinforcement learning (DRL) methods have impro…
cs.AI2026
Exploration and Online Transfer with Behavioral Foundation Models
Louis Bagot, Mathieu Lefort, Laëtitia Matignon
Zero-shot Transfer in Reinforcement Learning (RL) aims to train an agent that can generate optimal policies for any reward function, without additional learning at transfer time, w…
cs.LG2026
A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication
Valentin Cuzin-Rambaud, Laetitia Matignon, Maxime Morge
In multi-agent reinforcement learning (MARL), the integration of a communication mechanism, allowing agents to better learn to coordinate their actions and converge on their object…