Pierre-Luc Bacon

Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments

Ziyan Luo

Tianwei Ni

Xujie Si

2025-05-31

ArXiv (preprint)

Understanding the Effectiveness of Learning Behavioral Metrics in Deep Reinforcement Learning

Ziyan Luo

Tianwei Ni

Xujie Si

A key approach to state abstraction is approximating behavioral metrics (notably, bisimulation metrics) in the observation space, and embed … (see more)these learned distances in the representation space. While promising for robustness to task-irrelevant noise shown in prior work, accurately estimating these metrics remains challenging, requiring various design choices that create gaps between theory and practice. Prior evaluations focus mainly on final returns, leaving the quality of learned metrics and the source of performance gains unclear. To systematically assess how metric learning works in deep RL, we evaluate five recent approaches. We unify them under isometric embedding, identify key design choices, and benchmark them with baselines across 20 state-based and 14 pixel-based tasks, spanning 250+ configurations with diverse noise settings. Beyond final returns, we introduce the denoising factor to quantify the encoder’s ability to filter distractions. To further isolate the effect of metric learning, we propose an isolated metric estimation setting, where the encoder is influenced solely by the metric loss. Our results show that metric learning improves return and denoising only marginally, as its benefits fade when key design choices, such as layer normalization and self-prediction loss, are incorporated into the baseline. We also find that commonly used benchmarks (e.g., grayscale videos, varying state-based Gaussian noise dimensions) add little difficulty, while Gaussian noise with random projection and pixel-based Gaussian noise remain challenging even for the best methods. Finally, we release an open-source, modular codebase to improve reproducibility and support future research on metric learning in deep RL.

2025-05-09

rl-conference.cc/RLC/2025/Conference (published)

Understanding the Effectiveness of Learning Behavioral Metrics in Deep Reinforcement Learning

Ziyan Luo

Tianwei Ni

Xujie Si

A key approach to state abstraction is approximating behavioral metrics (notably, bisimulation metrics) in the observation space, and embed … (see more)these learned distances in the representation space. While promising for robustness to task-irrelevant noise shown in prior work, accurately estimating these metrics remains challenging, requiring various design choices that create gaps between theory and practice. Prior evaluations focus mainly on final returns, leaving the quality of learned metrics and the source of performance gains unclear. To systematically assess how metric learning works in deep RL, we evaluate five recent approaches. We unify them under isometric embedding, identify key design choices, and benchmark them with baselines across 20 state-based and 14 pixel-based tasks, spanning 250+ configurations with diverse noise settings. Beyond final returns, we introduce the denoising factor to quantify the encoder’s ability to filter distractions. To further isolate the effect of metric learning, we propose an isolated metric estimation setting, where the encoder is influenced solely by the metric loss. Our results show that metric learning improves return and denoising only marginally, as its benefits fade when key design choices, such as layer normalization and self-prediction loss, are incorporated into the baseline. We also find that commonly used benchmarks (e.g., grayscale videos, varying state-based Gaussian noise dimensions) add little difficulty, while Gaussian noise with random projection and pixel-based Gaussian noise remain challenging even for the best methods. Finally, we release an open-source, modular codebase to improve reproducibility and support future research on metric learning in deep RL.

2025-05-09

rl-conference.cc/RLC/2025/Conference (accepted)

Mol-MoE: Training Preference-Guided Routers for Molecule Generation

Diego Calanzone

Pierluca D'Oro

Recent advances in language models have enabled framing molecule generation as sequence modeling. However, existing approaches often rely on… (see more) single-objective reinforcement learning, limiting their applicability to real-world drug design, where multiple competing properties must be optimized. Traditional multi-objective reinforcement learning (MORL) methods require costly retraining for each new objective combination, making rapid exploration of trade-offs impractical. To overcome these limitations, we introduce Mol-MoE, a mixture-of-experts (MoE) architecture that enables efficient test-time steering of molecule generation without retraining. Central to our approach is a preference-based router training objective that incentivizes the router to combine experts in a way that aligns with user-specified trade-offs. This provides improved flexibility in exploring the chemical property space at test time, facilitating rapid trade-off exploration. Benchmarking against state-of-the-art methods, we show that Mol-MoE achieves superior sample quality and steerability.

2025-03-05

ICLR.cc/2025/Workshop/GEM (published)

Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons

Simon Dufort-Labbé

Pierluca D'Oro

Evgenii Nikishin

Razvan Pascanu

Irina Rish

Aristide Baratin

2025-02-13

TMLR (accepted)

Mol-MoE: Training Preference-Guided Routers for Molecule Generation

Diego Calanzone

Pierluca D'Oro

Recent advances in language models have enabled framing molecule generation as sequence modeling. However, existing approaches often rely on… (see more) single-objective reinforcement learning, limiting their applicability to real-world drug design, where multiple competing properties must be optimized. Traditional multi-objective reinforcement learning (MORL) methods require costly retraining for each new objective combination, making rapid exploration of trade-offs impractical. To overcome these limitations, we introduce Mol-MoE, a mixture-of-experts (MoE) architecture that enables efficient test-time steering of molecule generation without retraining. Central to our approach is a preference-based router training objective that incentivizes the router to combine experts in a way that aligns with user-specified trade-offs. This provides improved flexibility in exploring the chemical property space at test time, facilitating rapid trade-off exploration. Benchmarking against state-of-the-art methods, we show that Mol-MoE achieves superior sample quality and steerability.

2025-02-08

ArXiv (preprint)

MaestroMotif: Skill Design from Artificial Intelligence Feedback

Martin Klissarov

Mikael Henaff

Roberta Raileanu

Shagun Sodhani

Pascal Vincent

Amy Zhang

Marlos C. Machado

Pierluca D'Oro

Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an… (see more) AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create and reuse skills. It first uses an LLM's feedback to automatically design rewards corresponding to each skill, starting from their natural language description. Then, it employs an LLM's code generation abilities, together with reinforcement learning, for training the skills and combining them to implement complex behaviors specified in language. We evaluate MaestroMotif using a suite of complex tasks in the NetHack Learning Environment (NLE), demonstrating that it surpasses existing approaches in both performance and usability.

2025-01-22

ICLR.cc/2025/Conference (oral)

MaestroMotif: Skill Design from Artificial Intelligence Feedback

Martin Klissarov

Mikael Henaff

Roberta Raileanu

Shagun Sodhani

Pascal Vincent

Amy Zhang

Marlos C. Machado

Pierluca D'Oro

Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an… (see more) AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create and reuse skills. It first uses an LLM's feedback to automatically design rewards corresponding to each skill, starting from their natural language description. Then, it employs an LLM's code generation abilities, together with reinforcement learning, for training the skills and combining them to implement complex behaviors specified in language. We evaluate MaestroMotif using a suite of complex tasks in the NetHack Learning Environment (NLE), demonstrating that it surpasses existing approaches in both performance and usability.

2024-12-11

ArXiv (preprint)

MaestroMotif: Skill Design from Artificial Intelligence Feedback

Martin Klissarov

Mikael Henaff

Roberta Raileanu

Shagun Sodhani

Pascal Vincent

Amy Zhang

Marlos C. Machado

Pierluca D'Oro

Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an… (see more) AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create and reuse skills. It first uses an LLM's feedback to automatically design rewards corresponding to each skill, starting from their natural language description. Then, it employs an LLM's code generation abilities, together with reinforcement learning, for training the skills and combining them to implement complex behaviors specified in language. We evaluate MaestroMotif using a suite of complex tasks in the NetHack Learning Environment (NLE), demonstrating that it surpasses existing approaches in both performance and usability.

2024-12-11

ArXiv (preprint)

Neural differential equations for temperature control in buildings under demand response programs

Vincent Taboga

Clement Gehring

Mathieu Le Cam

Hanane Dagdougui

2024-08-01

Applied Energy (published)

Neural differential equations for temperature control in buildings under demand response programs

Vincent Taboga

Clement Gehring

Mathieu Le Cam

Hanane Dagdougui

2024-08-01

Applied Energy (published)