Author name cluster

Merwan Barlier

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

2 author rows

ICML Conference 2025 Conference Paper

Adaptive Sample Sharing for Multi Agent Linear Bandits

Hamza Cherkaoui
Merwan Barlier
Igor Colin

The multi-agent linear bandit setting is a well-known setting for which designing efficient collaboration between agents remains challenging. This paper studies the impact of data sharing among agents on regret minimization. Unlike most existing approaches, our contribution does not rely on any assumptions on the bandit parameters structure. Our main result formalizes the trade-off between the bias and uncertainty of the bandit parameter estimation for efficient collaboration. This result is the cornerstone of the Bandit Adaptive Sample Sharing (BASS) algorithm, whose efficiency over the current state-of-the-art is validated through both theoretical analysis and empirical evaluations on both synthetic and real-world datasets. Furthermore, we demonstrate that, when agents’ parameters display a cluster structure, our algorithm accurately recovers them.

Details

IJCAI Conference 2024 Conference Paper

A Conservative Approach for Few-Shot Transfer in Off-Dynamics Reinforcement Learning

Paul Daoudi
CHRISTOPHE PRIEUR
Bogdan Robu
Merwan Barlier
Ludovic Dos Santos

Off-dynamics Reinforcement Learning (ODRL) seeks to transfer a policy from a source environment to a target environment characterized by distinct yet similar dynamics. In this context, traditional RL agents depend excessively on the dynamics of the source environment, resulting in the discovery of policies that excel in this environment but fail to provide reasonable performance in the target one. In the few-shot framework, a limited number of transitions from the target environment are introduced to facilitate a more effective transfer. Addressing this challenge, we propose an innovative approach inspired by recent advancements in Imitation Learning and Conservative RL algorithms. This method introduces a penalty to regulate the trajectories generated by the source-trained policy. We evaluate our method across various environments representing diverse off-dynamics conditions, where access to the target environment is extremely limited. These experiments include high-dimensional systems relevant to real-world applications. Across most tested scenarios, our proposed method demonstrates performance improvements compared to existing baselines.

PDF Details DOI

EWRL Workshop 2024 Workshop Paper

A Conservative Approach for Few-Shot Transfer in Off-Dynamics Reinforcement Learning

Paul Daoudi
CHRISTOPHE PRIEUR
Bogdan Robu
Merwan Barlier
Ludovic Dos Santos

Off-dynamics Reinforcement Learning (ODRL) seeks to transfer a policy from a source environment to a target environment characterized by distinct yet similar dynamics. In this context, traditional RL agents depend excessively on the dynamics of the source environment, resulting in the discovery of policies that excel in this environment but fail to provide reasonable performance in the target one. In the few-shot framework, a limited number of transitions from the target environment are introduced to facilitate a more effective transfer. Addressing this challenge, we propose a new approach inspired by recent advancements in Imitation Learning and conservative RL algorithms. The proposed method introduces a penalty to regulate the trajectories generated by the source-trained policy. We evaluate our method across various environments representing diverse off-dynamics conditions, where it demonstrates performance improvements compared to existing baselines across most tested scenarios.

PDF

EWRL Workshop 2024 Workshop Paper

Differentially Private Deep Model-Based Reinforcement Learning

Alexandre Rio
Merwan Barlier
Igor Colin
Albert Thomas

We address deep offline reinforcement learning with privacy guarantees, where the goal is to train a policy that is differentially private with respect to individual trajectories in the dataset. To achieve this, we introduce DP-MORL, an MBRL algorithm with differential privacy guarantees. A private model of the environment is first learned from offline data using DP-FedAvg, a training method for neural networks that provides differential privacy guarantees at the trajectory level. Then, we use model-based policy optimization to derive a policy from the (penalized) private model, without any further interaction with the system or access to the dataset. We empirically show that DP-MORL enables the training of private RL agents from offline data in continuous control tasks and we furthermore outline the price of privacy in this setting.

PDF

ICML Conference 2024 Conference Paper

Measures of diversity and space-filling designs for categorical data

Cédric Malherbe
Emilio Domínguez-Sánchez
Merwan Barlier
Igor Colin
Haitham Bou-Ammar
Tom Diethe

Selecting a small subset of items that represent the diversity of a larger population lies at the heart of many data analysis and machine learning applications. However, when it comes to items described by discrete features, the lack of natural ordering and the combinatorial nature of the search space pose significant challenges to the current selection techniques and make existing methods ill-suited. In this paper, we propose to make a step in that direction by proposing novel methods to select subsets of diverse categorical data based on the advances in combinatorial optimization. First, we start to cast the subset selection problem through the lens of the optimization of three diversity metrics. We then provide novel bounds for this problem and present exact solvers that unfortunately come with a high computational cost. To overcome this bottleneck, we go on and show how to employ tools from linear programming and submodular optimization by introducing two computationally plausible methods that still present approximation guarantees about the diversity metrics. Finally, a numerical assessment is provided to illustrate the potential of the designs with respect to state-of-the-art methods.

Details

AAMAS Conference 2023 Conference Paper

Enhancing Reinforcement Learning Agents with Local Guides

Paul Daoudi
Bogdan Robu
CHRISTOPHE PRIEUR
Ludovic Dos Santos
Merwan Barlier

This paper addresses the problem of integrating local guide policies into a Reinforcement Learning agent. For this, we show how to adapt existing algorithms to this setting before introducing a novel algorithm based on a noisy policy-switching procedure. This approach builds on a proper Approximate Policy Evaluation (APE) scheme to provide a perturbation that carefully leads the local guides towards better actions. We evaluated our method on a set of classical Reinforcement Learning problems, including safetycritical systems where the agent cannot enter some areas at the risk of triggering catastrophic consequences. In all the proposed environments, our agent proved to be efficient at leveraging those policies to improve the performance of any APE-based Reinforcement Learning algorithm, especially in its first learning stages.

PDF

ICML Conference 2023 Conference Paper

Multi-Agent Best Arm Identification with Private Communications

Alexandre Rio
Merwan Barlier
Igor Colin
Marta Soare

We address multi-agent best arm identification with privacy guarantees. In this setting, agents collaborate by communicating to find the optimal arm. To avoid leaking sensitive data through messages, we consider two notions of privacy withholding different kinds of information: differential privacy and $(\epsilon, \eta)$-privacy. For each privacy definition, we propose an algorithm based on a two-level successive elimination scheme. We provide theoretical guarantees for the privacy level, accuracy and sample complexity of our algorithms. Experiments on various settings support our theoretical findings.

Details

NeurIPS Conference 2020 Conference Paper

A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration

Kevin Scaman
Ludovic Dos Santos
Merwan Barlier
Igor Colin

This work proposes a novel smoothing method, called Bend, Mix and Release (BMR), that extends two well-known smooth approximations of the convex optimization literature: randomized smoothing and the Moreau envelope. The BMR smoothing method allows to trade-off between the computational simplicity of randomized smoothing (RS) and the approximation efficiency of the Moreau envelope (ME). More specifically, we show that BMR achieves up to a $\sqrt{d}$ multiplicative improvement compared to the approximation error of RS, where $d$ is the dimension of the search space, while being less computation intensive than the ME. For non-convex objectives, BMR also has the desirable property to widen local minima, allowing optimization methods to reach small cracks and crevices of extremely irregular and non-convex functions, while being well-suited to a distributed setting. This novel smoothing method is then used to improve first-order non-smooth optimization (both convex and non-convex) by allowing for a local exploration of the search space. More specifically, our analysis sheds light on the similarities between evolution strategies and BMR, creating a link between exploration strategies of zeroth-order methods and the regularity of first-order optimization problems. Finally, we evidence the impact of BMR through synthetic experiments.

PDF Details

AAMAS Conference 2018 Conference Paper

Training Dialogue Systems With Human Advice

Merwan Barlier
Romain Laroche
Olivier Pietquin

One major drawback of Reinforcement Learning (RL) Spoken Dialogue Systems is that they inherit from the general exploration requirements of RL which makes them hard to deploy from an industry perspective. On the other hand, industrial systems rely on human expertise and hand written rules so as to avoid irrelevant behavior to happen and maintain acceptable experience from the user point of view. In this paper, we attempt to bridge the gap between those two worlds by providing an easy way to incorporate all kinds of human expertise in the training phase of a Reinforcement Learning Dialogue System. Our approach, based on the TAMER framework, enables safe and efficient policy learning by combining the traditional Reinforcement Learning reward signal with an additional reward, encoding expert advice. Experimental results show that our method leads to substantial improvements over more traditional Reinforcement Learning methods.

PDF

AAAI Conference 2017 Conference Paper

Transfer Reinforcement Learning with Shared Dynamics

Romain Laroche
Merwan Barlier

This article addresses a particular Transfer Reinforcement Learning (RL) problem: when dynamics do not change from one task to another, and only the reward function does. Our method relies on two ideas, the ﬁrst one is that transition samples obtained from a task can be reused to learn on any other task: an immediate reward estimator is learnt in a supervised fashion and for each sample, the reward entry is changed by its reward estimate. The second idea consists in adopting the optimism in the face of uncertainty principle and to use upper bound reward estimates. Our method is tested on a navigation task, under four Transfer RL experimental settings: with a known reward function, with strong and weak expert knowledge on the reward function, and with a completely unknown reward function. It is also evaluated in a Multi-Task RL experiment and compared with the state-of-the-art algorithms. Results reveal that this method constitutes a major improvement for transfer/multi-task problems that share dynamics.

PDF Details