91 citations · 148 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
Task-conditioned adaptation of visual features in multi-task policy learning
Pierre Marza, Laetitia Matignon, Olivier Simonin +1
Successfully addressing a wide variety of tasks is a core ability of autonomous agents, requiring flexibly adapting the underlying decision-making strategies and, as we argue in th…
Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning
Erwan Escudie, Laetitia Matignon, Jacques Saraydaryan
Learning robot navigation strategies among pedestrian is crucial for domain based applications. Combining perception, planning and prediction allows us to model the interactions be…
AutoNeRF: Training Implicit Scene Representations with Autonomous Agents
Pierre Marza, Laetitia Matignon, Olivier Simonin +3
Implicit representations such as Neural Radiance Fields (NeRF) have been shown to be very effective at novel view synthesis. However, these models typically require manual and care…
Multi-Object Navigation with dynamically learned neural implicit representations
Pierre Marza, Laetitia Matignon, Olivier Simonin +1
Understanding and mapping a new environment are core abilities of any autonomously navigating agent. While classical robotics usually estimates maps in a stand-alone manner with SL…
An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey
Arthur Aubret, Laetitia Matignon, Salima Hassas
The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL). However a n…
Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation
Pierre Marza, Laetitia Matignon, Olivier Simonin +1
In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently re…
DisTop: Discovering a Topological representation to learn diverse and rewarding skills
Arthur Aubret, Laetitia matignon, Salima Hassas
The optimal way for a deep reinforcement learning (DRL) agent to explore is to learn a set of skills that achieves a uniform distribution of states. Following this,we introduce Dis…
ELSIM: End-to-end learning of reusable skills through intrinsic motivation
Arthur Aubret, Laetitia Matignon, Salima Hassas
Taking inspiration from developmental learning, we present a novel reinforcement learning architecture which hierarchically learns and represents self-generated skills in an end-to…
A survey on intrinsic motivation in reinforcement learning
Arthur Aubret, Laetitia Matignon, Salima Hassas
The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL). However a n…