40 citations · 134 across the 40 of their papers we have counts for
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Behavioral Foundation Models for Quality Diversity
Nazim Bendib, Nicolas Perrin-Gilbert, Olivier Sigaud
Behavioral Foundation Models (BFMs) are an emerging paradigm in reinforcement learning, playing a role analogous to large language models in natural language processing: they have…
Locally-Guided Actor-Critic: Training a Goal-conditioned Actor with a Subgoal-aware Critic
Olivier Serris, Stéphane Doncieux, Olivier Sigaud
Goal-conditioned reinforcement learning struggles with long horizons when rewards are sparse. While a planner can provide subgoals to guide a low-level policy, its use at test time…
MODIP: Efficient Model-Based Optimization for Diffusion Policies
Zakariae El Asri, Philippe Gratias-Quiquandon, Nicolas Thome +1
Diffusion policies (DPs) have emerged as expressive policy representations for robot learning, often used with imitation learning methods such as behavioral cloning (BC). However,…
RT-HCP: Dealing with Inference Delays and Sample Efficiency to Learn Directly on Robotic Platforms
Zakariae El Asri, Ibrahim Laiche, Clément Rambour +2
Learning a controller directly on the robot requires extreme sample efficiency. Model-based reinforcement learning (RL) methods are the most sample efficient, but they often suffer…
HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents
Thomas Carta, Clément Romac, Loris Gaven +3
We study goal-conditioned reinforcement learning in partially observable environments with sparse rewards and large, structured goal spaces. In such settings, complex goals often r…
Imagine Beyond! Distributionally Robust Auto-Encoding for State Space Coverage in Online Reinforcement Learning
Nicolas Castanet, Olivier Sigaud, Sylvain Lamprier
Goal-Conditioned Reinforcement Learning (GCRL) enables agents to autonomously acquire diverse behaviors, but faces major challenges in visual environments due to high-dimensional,…
A tale of two goals: leveraging sequentiality in multi-goal scenarios
Olivier Serris, Stéphane Doncieux, Olivier Sigaud
Several hierarchical reinforcement learning methods leverage planning to create a graph or sequences of intermediate goals, guiding a lower-level goal-conditioned (GC) policy to re…
VIPER: Visual Perception and Explainable Reasoning for Sequential Decision-Making
Mohamed Salim Aissi, Clemence Grislain, Mohamed Chetouani +3
While Large Language Models (LLMs) excel at reasoning on text and Vision-Language Models (VLMs) are highly effective for visual perception, applying those models for visual instruc…
Reinforcement Learning for Aligning Large Language Models Agents with Interactive Environments: Quantifying and Mitigating Prompt Overfitting
Mohamed Salim Aissi, Clement Romac, Thomas Carta +5
Reinforcement learning (RL) is a promising approach for aligning large language models (LLMs) knowledge with sequential decision-making tasks. However, few studies have thoroughly…
SAC-GLAM: Improving Online RL for LLM agents with Soft Actor-Critic and Hindsight Relabeling
Loris Gaven, Clement Romac, Thomas Carta +3
The past years have seen Large Language Models (LLMs) strive not only as generative models but also as agents solving textual sequential decision-making tasks. When facing complex…
Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement Learning
Zakariae El Asri, Olivier Sigaud, Nicolas Thome
Applying reinforcement learning (RL) to real-world applications requires addressing a trade-off between asymptotic performance, sample efficiency, and inference time. In this work,…
Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning
Thomas Carta, Clément Romac, Thomas Wolf +3
Recent works successfully leveraged Large Language Models' (LLM) abilities to capture abstract knowledge about world's physics to solve decision-making problems. Yet, the alignment…
Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning
Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
Teaching an agent to perform new tasks using natural language can easily be hindered by ambiguities in interpretation. When a teacher provides an instruction to a learner about an…
Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning
Nicolas Castanet, Sylvain Lamprier, Olivier Sigaud
In multi-goal Reinforcement Learning, an agent can share experience between related training tasks, resulting in better generalization for new tasks at test time. However, when the…
Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments
Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
Learning from demonstration methods usually leverage close to optimal demonstrations to accelerate training. By contrast, when demonstrating a task, human teachers deviate from opt…
Pedagogical Demonstrations and Pragmatic Learning in Artificial Tutor-Learner Interactions
Hugo Caselles-Dupré, Mohamed Chetouani, Olivier Sigaud
When demonstrating a task, human tutors pedagogically modify their behavior by either "showing" the task rather than just "doing" it (exaggerating on relevant parts of the demonstr…
Towards Teachable Autotelic Agents
Olivier Sigaud, Ahmed Akakzia, Hugo Caselles-Dupré +3
Autonomous discovery and direct instruction are two distinct sources of learning in children but education sciences demonstrate that mixed approaches such as assisted discovery or…
Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey
Cédric Colas, Tristan Karch, Olivier Sigaud +1
Building autonomous machines that can explore open-ended environments, discover possible interactions and build repertoires of skills is a general objective of artificial intellige…
Language-Conditioned Goal Generation: a New Approach to Language Grounding for RL
Cédric Colas, Ahmed Akakzia, Pierre-Yves Oudeyer +2
In the real world, linguistic agents are also embodied agents: they perceive and act in the physical world. The notion of Language Grounding questions the interactions between lang…
To Share or Not To Share: A Comprehensive Appraisal of Weight-Sharing
Aloïs Pourchot, Alexis Ducarouge, Olivier Sigaud
Weight-sharing (WS) has recently emerged as a paradigm to accelerate the automated search for efficient neural architectures, a process dubbed Neural Architecture Search (NAS). Alt…
The problem with DDPG: understanding failures in deterministic environments with sparse rewards
Guillaume Matheron, Nicolas Perrin, Olivier Sigaud
In environments with continuous state and action spaces, state-of-the-art actor-critic reinforcement learning algorithms can solve very complex problems, yet can also fail in envir…