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Priyesh Vijayan

vijayanp@mila.quebec
Doctorat - McGill
Superviseur⋅e principal⋅e
Doina Precup
Co-supervisor
Samira Ebrahimi Kahou
Sujets de recherche
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage spectral
Apprentissage sur graphes
Réseaux de neurones en graphes
Site web
Google Scholar
GitHub

Publications

Addressing Sparse-Rewards in RL with Scalable Hierarchical Novel Eigen Options
Priyesh Vijayan
Élodie Coté-Gauthier
Mathieu Reymond
Sarath Chandar
Doina Precup
Isabeau Prémont-Schwarz
Temporally extended exploration via graph Laplacian-based options is a promising approach to sparse-reward reinforcement learning (RL), but … (voir plus)existing methods either do not explicitly target novelty or fail to scale to pixel-based domains under function approximation. Novel Exploration via Orthogonality (NEO) addresses the first issue by constructing options that navigate from highly visited regions toward less visited ones, yet prior results were limited to settings where exact eigenvectors can be computed. We present a scalable extension of NEO to pixel-based domains, built on three contributions. First, we use a novelty-weighted continuous Laplacian graph-drawing objective, which enables RL with continuous observations. Second, we embed the resulting eigen-potential options within a hierarchical reinforcement learning framework, enabling coherent temporally extended behavior. Third, we observe that learned eigen-potential rewards are directional but locally unreliable under online approximation; we therefore augment each option reward with a novelty bonus, a novel design idea that proves essential for stabilizing option learning while preserving novelty-directed exploration. Together, these contributions yield stronger and more persistent exploration, enabling longer option rollouts and better access to hard-to-reach novel states. Empirically, our method significantly outperforms both the prior scalable Laplacian-option baseline and a direct extension of NEO on sparse-reward benchmarks under a fixed budget. On Montezuma's Revenge, our best variant achieves approximately 1.8x higher return than both baselines. On Venture, both baselines yield returns near zero, whereas our method achieves a return of 1135. Across seven hard ProcGen games, our method achieves approximately 3.5x and 5.6x higher aggregate normalized return than the two baselines, respectively.
2026-07-20
RLBRew @ Reinforcement Learning Conference (publié)
openreview.net
openreview.net
Revisiting Laplacian Representations for Value Function Approximation in Deep RL
Priyesh Vijayan
Padideh Nouri
Rishav
A. Chandar
Yash Chandak
Mathieu Reymond
S Ebrahimi Kahou
Doina Precup
Proto-value functions (PVFs) introduced Laplacian embeddings as an effective feature basis for value-function approximation; however, their … (voir plus)utility remained limited to small, fully known state spaces. Recent work has scaled Laplacian embeddings to high-dimensional inputs, using them for reward shaping and option discovery in goal-directed tasks, yet only as auxiliary signals, rather than directly using them as features for value functions. In this paper, we learn Laplacian eigenvectors online and employ them as features for Q-learning in 23 Atari games. We empirically demonstrate that these online–learned embeddings substantially improve model-free RL in large, high-dimensional domains. We demonstrate that enriching state representations with action embeddings yields additional gains under both behavior-policy and uniform-random policies. Additionally, we introduce the Fusion architecture, which augments the representation with useful inductive bias at the embedding level. To assess the usefulness of each embedding used in the Fusion architecture, we use Shapley values analysis.
2025-06-21
rl-conference.cc/RLC/2025/Workshop/IBRL (publié)
openreview.net
openreview.net
Generative Active Learning for the Search of Small-Molecule Protein Binders
Maksym Korablyov
Cheng-Hao Liu
Moksh Jain
Almer Van Der Sloot
Éric Jolicoeur
Edward Ruediger
Andrei Nica
Emmanuel Bengio
Kostiantyn Lapchevskyi
Daniel St-Cyr
Doris Alexandra Schuetz
Victor Ion Butoi
Jarrid Rector-Brooks
Simon Blackburn
Leo Feng
Hadi Nekoei
Saikrishna Gottipati
Priyesh Vijayan
Prateek Gupta
Ladislav Rampasek … (voir 14 de plus)
Sasikanth Avancha
Pierre-Luc Bacon
William Hamilton
Brooks Paige
Sanchit Misra
Stanislaw Jastrzebski
Bharat Kaul
Doina Precup
José Miguel Hernández-Lobato
Marwin Segler
Michael Bronstein
Anne Marinier
Mike Tyers
Yoshua Bengio
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exh… (voir plus)ibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.
2023-12-31
arXiv (prépublication)
doi.org
arxiv.org