Optimal discounting for offline input-driven MDP
Randy Lefebvre
Audrey Durand
Offline reinforcement learning has gained a lot of popularity for its potential to solve industry challenges. However, real-world environmen… (see more)ts are often highly stochastic and partially observable, leading long-term planners to overfit to offline data in model-based settings. Input-driven Markov Decision Processes (IDMDPs) offer a way to work with some of the uncertainty by letting designers separate what the agent has control over (states) from what it cannot (inputs) in the environnement. These stochastic external inputs are often difficult to model. Under the assumption that the input model will be imperfect, we investigate the bias-variance tradeoff under shallow planning in IDMDPs. Paving the way to input-driven planning horizons, we also investigate the similarity of optimal planning horizons at different inputs given the structure of the input space.
2025-05-09
rl-conference.cc/RLC/2025/Conference (published)
openreview.net
openreview.net
Optimal discounting for offline input-driven MDP
Randy Lefebvre
Audrey Durand
Offline reinforcement learning has gained a lot of popularity for its potential to solve industry challenges. However, real-world environmen… (see more)ts are often highly stochastic and partially observable, leading long-term planners to overfit to offline data in model-based settings. Input-driven Markov Decision Processes (IDMDPs) offer a way to work with some of the uncertainty by letting designers separate what the agent has control over (states) from what it cannot (inputs) in the environnement. These stochastic external inputs are often difficult to model. Under the assumption that the input model will be imperfect, we investigate the bias-variance tradeoff under shallow planning in IDMDPs. Paving the way to input-driven planning horizons, we also investigate the similarity of optimal planning horizons at different inputs given the structure of the input space.
2025-05-09
rl-conference.cc/RLC/2025/Conference (accepted)
openreview.net
openreview.net
Optimistic critics can empower small actors
Olya Mastikhina
Dhruv Sreenivas
Pablo Samuel Castro
Actor-critic methods have been central to many of the recent advances in deep reinforcement learning. The most common approach is to use _sy… (see more)mmetric_ architectures, whereby both actor and critic have the same network topology and number of parameters. However, recent works have argued for the advantages of _asymmetric_ setups, specifically with the use of smaller actors. We perform broad empirical investigations and analyses to better understand the implications of this and find that, in general, smaller actors result in performance degradation and overfit critics. Our analyses suggest _poor data collection_, due to value underestimation, as one of the main causes for this behavior, and further highlight the crucial role the critic can play in alleviating this pathology. We explore techniques to mitigate the observed value underestimation, which enables further research in asymmetric actor-critic methods.
2025-05-09
rl-conference.cc/RLC/2025/Conference (published)
openreview.net
openreview.net
Optimistic critics can empower small actors
Olya Mastikhina
Dhruv Sreenivas
Pablo Samuel Castro
Actor-critic methods have been central to many of the recent advances in deep reinforcement learning. The most common approach is to use _sy… (see more)mmetric_ architectures, whereby both actor and critic have the same network topology and number of parameters. However, recent works have argued for the advantages of _asymmetric_ setups, specifically with the use of smaller actors. We perform broad empirical investigations and analyses to better understand the implications of this and find that, in general, smaller actors result in performance degradation and overfit critics. Our analyses suggest _poor data collection_, due to value underestimation, as one of the main causes for this behavior, and further highlight the crucial role the critic can play in alleviating this pathology. We explore techniques to mitigate the observed value underestimation, which enables further research in asymmetric actor-critic methods.
2025-05-09
rl-conference.cc/RLC/2025/Conference (accepted)
openreview.net
openreview.net
Understanding the Effectiveness of Learning Behavioral Metrics in Deep Reinforcement Learning
Ziyan Luo
Tianwei Ni
Pierre-Luc Bacon
Doina Precup
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)
openreview.net
openreview.net
Understanding the Effectiveness of Learning Behavioral Metrics in Deep Reinforcement Learning
Ziyan Luo
Tianwei Ni
Pierre-Luc Bacon
Doina Precup
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)
openreview.net
openreview.net
Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity
Dhananjay Bhaskar
Jessica Moore
Feng Gao
Bastian Rieck
Firas Khasawneh
Elizabeth Munch
Valentina Greco
Smita Krishnaswamy
Neural signals are high-dimensional, noisy, and dynamic, making it challenging to extract interpretable features linked to behavior or disea… (see more)se. We introduce Neurospectrum, a framework that encodes neural activity as latent trajectories shaped by spatial and temporal structure. At each timepoint, signals are represented on a graph capturing spatial relationships, with a learnable attention mechanism highlighting important regions. These are embedded using graph wavelets and passed through a manifold-regularized autoencoder that preserves temporal geometry. The resulting latent trajectory is summarized using a principled set of descriptors - including curvature, path signatures, persistent homology, and recurrent networks -that capture multiscale geometric, topological, and dynamical features. These features drive downstream prediction in a modular, interpretable, and end-to-end trainable framework. We evaluate Neurospectrum on simulated and experimental datasets. It tracks phase synchronization in Kuramoto simulations, reconstructs visual stimuli from calcium imaging, and identifies biomarkers of obsessive-compulsive disorder in fMRI. Across tasks, Neurospectrum uncovers meaningful neural dynamics and outperforms traditional analysis methods.
2025-05-08
bioRxiv (preprint)
doi.org
Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers
Kusha Sareen
Morgane M Moss
Alessandro Sordoni
Rishabh Agarwal
Arian Hosseini
2025-05-07
ArXiv (preprint)
arxiv.org
arxiv.org
Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers
Kusha Sareen
Morgane M Moss
Alessandro Sordoni
Rishabh Agarwal
Arian Hosseini
2025-05-07
ArXiv (preprint)
arxiv.org
arxiv.org
Kernel-Level Event-Based Performance Anomaly Detection in Software Systems under Varying Load Conditions
Anthonia Njoku
Heng Li
Foutse Khomh
2025-05-05
Companion of the 16th ACM/SPEC International Conference on Performance Engineering (published)
doi.org
The Search for Squawk: Agile Modeling in Bioacoustics
Vincent Dumoulin
Otilia Stretcu
Jenny Hamer
Lauren Harrell
Rob Laber
Hugo Larochelle
Bart van Merriënboer
Amanda Navine
Patrick Hart
Ben Williams
Timothy A. C. Lamont
Tries B. Rasak
Mars Coral Restoration Team
Sheryn Brodie
Brendan Doohan
Philip Eichinski
Paul Roe
Lin Schwarzkopf
Tom Denton
2025-05-05
ArXiv (preprint)
arxiv.org
arxiv.org
The Search for Squawk: Agile Modeling in Bioacoustics
Vincent Dumoulin
Otilia Stretcu
Jenny Hamer
Lauren Harrell
Rob Laber
Hugo Larochelle
Bart van Merriënboer
Amanda Navine
Patrick Hart
Ben Williams
Timothy A. C. Lamont
Tries B. Rasak
Mars Coral Restoration Team
Sheryn Brodie
Brendan Doohan
Philip Eichinski
Paul Roe
Lin Schwarzkopf
Tom Denton
2025-05-05
ArXiv (preprint)
arxiv.org
arxiv.org