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Vincent Moens

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6 papers

2 author rows

JMLR Journal 2024 Journal Article

BenchMARL: Benchmarking Multi-Agent Reinforcement Learning

Matteo Bettini
Amanda Prorok
Vincent Moens

The field of Multi-Agent Reinforcement Learning (MARL) is currently facing a reproducibility crisis. While solutions for standardized reporting have been proposed to address the issue, we still lack a benchmarking tool that enables standardization and reproducibility, while leveraging cutting-edge Reinforcement Learning (RL) implementations. In this paper, we introduce BenchMARL, the first MARL training library created to enable standardized benchmarking across different algorithms, models, and environments. BenchMARL uses TorchRL as its backend, granting it high-performance and maintained state-of-the-art implementations while addressing the broad community of MARL PyTorch users. Its design enables systematic configuration and reporting, thus allowing users to create and run complex benchmarks from simple one-line inputs. BenchMARL is open-sourced on GitHub at https://github.com/facebookresearch/BenchMARL [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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NeurIPS Conference 2024 Conference Paper

BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO

Sebastian Dittert
Vincent Moens
Gianni De Fabritiis

We present BricksRL, a platform designed to democratize access to robotics for reinforcement learning research and education. BricksRL facilitates the creation, design, and training of custom LEGO robots in the real world by interfacing them with the TorchRL library for reinforcement learning agents. The integration of TorchRL with the LEGO hubs, via Bluetooth bidirectional communication, enables state-of-the-art reinforcement learning training on GPUs for a wide variety of LEGO builds. This offers a flexible and cost-efficient approach for scaling and also provides a robust infrastructure for robot-environment-algorithm communication. We present various experiments across tasks and robot configurations, providing built plans and training results. Furthermore, we demonstrate that inexpensive LEGO robots can be trained end-to-end in the real world to achieve simple tasks, with training times typically under 120 minutes on a normal laptop. Moreover, we show how users can extend the capabilities, exemplified by the successful integration of non-LEGO sensors. By enhancing accessibility to both robotics and reinforcement learning, BricksRL establishes a strong foundation for democratized robotic learning in research and educational settings.

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ICLR Conference 2024 Conference Paper

TorchRL: A data-driven decision-making library for PyTorch

Albert Bou
Matteo Bettini
Sebastian Dittert
Vikash Kumar
Shagun Sodhani
Xiaomeng Yang
Gianni De Fabritiis
Vincent Moens

PyTorch has ascended as a premier machine learning framework, yet it lacks a native and comprehensive library for decision and control tasks suitable for large development teams dealing with complex real-world data and environments. To address this issue, we propose TorchRL, a generalistic control library for PyTorch that provides well-integrated, yet standalone components. We introduce a new and flexible PyTorch primitive, the TensorDict, which facilitates streamlined algorithm development across the many branches of Reinforcement Learning (RL) and control. We provide a detailed description of the building blocks and an extensive overview of the library across domains and tasks. Finally, we experimentally demonstrate its reliability and flexibility, and show comparative benchmarks to demonstrate its computational efficiency. TorchRL fosters long-term support and is publicly available on GitHub for greater reproducibility and collaboration within the research community. The code is open-sourced on GitHub.

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NeurIPS Conference 2023 Conference Paper

RoboHive: A Unified Framework for Robot Learning

Vikash Kumar
Rutav Shah
Gaoyue Zhou
Vincent Moens
Vittorio Caggiano
Abhishek Gupta
Aravind Rajeswaran

We present RoboHive, a comprehensive software platform and ecosystem for research in the field of Robot Learning and Embodied Artificial Intelligence. Our platform encompasses a diverse range of pre-existing and novel environments, including dexterous manipulation with the Shadow Hand, whole-arm manipulation tasks with Franka and Fetch robots, quadruped locomotion, among others. Included environments are organized within and cover multiple domains such as hand manipulation, locomotion, multi-task, multi-agent, muscles, etc. In comparison to prior works, RoboHive offers a streamlined and unified task interface taking dependency on only a minimal set of well-maintained packages, features tasks with high physics fidelity and rich visual diversity, and supports common hardware drivers for real-world deployment. The unified interface of RoboHive offers a convenient and accessible abstraction for algorithmic research in imitation, reinforcement, multi-task, and hierarchical learning. Furthermore, RoboHive includes expert demonstrations and baseline results for most environments, providing a standard for benchmarking and comparisons. Details: https: //sites. google. com/view/robohive

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AAMAS Conference 2021 Conference Paper

Learning Correlated Communication Topology in Multi-Agent Reinforcement learning

Yali Du
Bo Liu
Vincent Moens
Ziqi Liu
Zhicheng Ren
Jun Wang
Xu Chen
Haifeng Zhang

Communication improves the efficiency and convergence of multiagent learning. Existing study of agent communication has been limited on predefined fixed connections. While an attention mechanism exists and is useful for scheduling the communication between agents, it, however, largely ignores the dynamical nature of communication and thus the correlation between agents’ connections. In this work, we adopt a normalizing flow to encode correlation between agents interactions. The dynamical communication topology is directly learned by maximizing the agent rewards. In our end-to-end formulation, the communication structure is learned by considering it as a hidden dynamical variable. We realize centralized training of critics and graph reasoning policy, and decentralized execution from local observation and message that are received through the learned dynamical communication topology. Experiments on cooperative navigation in the particle world and adaptive traffic control tasks demonstrate the effectiveness of our method.

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ICML Conference 2018 Conference Paper

The Hierarchical Adaptive Forgetting Variational Filter

Vincent Moens

A common problem in Machine Learning and statistics consists in detecting whether the current sample in a stream of data belongs to the same distribution as previous ones, is an isolated outlier or inaugurates a new distribution of data. We present a hierarchical Bayesian algorithm that aims at learning a time-specific approximate posterior distribution of the parameters describing the distribution of the data observed. We derive the update equations of the variational parameters of the approximate posterior at each time step for models from the exponential family, and show that these updates find interesting correspondents in Reinforcement Learning (RL). In this perspective, our model can be seen as a hierarchical RL algorithm that learns a posterior distribution according to a certain stability confidence that is, in turn, learned according to its own stability confidence. Finally, we show some applications of our generic model, first in a RL context, next with an adaptive Bayesian Autoregressive model, and finally in the context of Stochastic Gradient Descent optimization.

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