Portrait de Esther Derman

Esther Derman

Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e
Pierre-Luc Bacon
Co-supervisor
Erick Delage
Sujets de recherche
Apprentissage par renforcement
Site web
Google Scholar
GitHub

Publications

Reward Redistribution for CVaR MDPs using a Bellman Operator on L-infinity
Aneri Muni
Vincent Taboga
Esther Derman
Pierre-Luc Bacon
Erick Delage
Tail-end risk measures such as static conditional value-at-risk (CVaR) are used in safety-critical applications to prevent rare, yet catastr… (voir plus)ophic events. Unlike risk-neutral objectives, the static CVaR of the return depends on entire trajectories without admitting a recursive Bellman decomposition in the underlying Markov decision process. A classical resolution relies on state augmentation with a continuous variable. However, unless restricted to a specialized class of admissible value functions, this formulation induces sparse rewards and degenerate fixed points. In this work, we propose a novel formulation of the static CVaR objective based on augmentation. Our alternative approach leads to a Bellman operator with: (1) dense per-step rewards; (2) contracting properties on the full space of bounded value functions. Building on this theoretical foundation, we develop risk-averse value iteration and model-free Q-learning algorithms that rely on discretized augmented states. We further provide convergence guarantees and approximation error bounds due to discretization. Empirical results demonstrate that our algorithms successfully learn CVaR-sensitive policies and achieve effective performance-safety trade-offs.
2026-02-02
ArXiv (prépublication)
arxiv.org
arxiv.org
Long-Horizon Model-Based Offline Reinforcement Learning Without Conservatism
Tianwei Ni
Esther Derman
Vineet Jain
Vincent Taboga
Siamak Ravanbakhsh
Pierre-Luc Bacon
2025-12-03
arXiv (prépublication)
doi.org
arxiv.org
State Entropy Regularization for Robust Reinforcement Learning
Yonatan Ashlag
Uri Koren
Mirco Mutti
Esther Derman
Pierre-Luc Bacon
Shie Mannor
State entropy regularization has empirically shown better exploration and sample complexity in reinforcement learning (RL). However, its the… (voir plus)oretical guarantees have not been studied. In this paper, we show that state entropy regularization improves robustness to structured and spatially correlated perturbations. These types of variation are common in transfer learning but often overlooked by standard robust RL methods, which typically focus on small, uncorrelated changes. We provide a comprehensive characterization of these robustness properties, including formal guarantees under reward and transition uncertainty, as well as settings where the method performs poorly. Much of our analysis contrasts state entropy with the widely used policy entropy regularization, highlighting their different benefits. Finally, from a practical standpoint, we illustrate that compared with policy entropy, the robustness advantages of state entropy are more sensitive to the number of rollouts used for policy evaluation.
2025-09-17
NeurIPS.cc/2025/Conference (présentation orale)
doi.org
openreview.net
What Matters when Modeling Human Behavior using Imitation Learning?
Aneri Muni
Esther Derman
Vincent Taboga
Pierre-Luc Bacon
Erick Delage
As AI systems become increasingly embedded in human decision-making process, aligning their behavior with human values is critical to ensuri… (voir plus)ng safe and trustworthy deployment. A central approach to AI Alignment called Imitation Learning (IL), trains a learner to directly mimic desirable human behaviors from expert demonstrations. However, standard IL methods assume that (1) experts act to optimize expected returns; (2) expert policies are Markovian. Both assumptions are inconsistent with empirical findings from behavioral economics, according to which humans are (1) risk-sensitive; and (2) make decisions based on past experience. In this work, we examine the implications of risk sensitivity for IL and show that standard approaches do not capture all optimal policies under risk-sensitive decision criteria. By characterizing these expert policies, we identify key limitations of existing IL algorithms in replicating expert performance in risk-sensitive settings. Our findings underscore the need for new IL frameworks that account for both risk-aware preferences and temporal dependencies to faithfully align AI behavior with human experts.
2025-06-09
ICML.cc/2025/Workshop/MoFA (poster)
openreview.net
openreview.net
Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learning
Marco Jiralerspong
Esther Derman
Danilo Vucetic
Nikolay Malkin
Bilun Sun
Tianyu Zhang
Pierre-Luc Bacon
Gauthier Gidel
A major bottleneck in scientific discovery consists of narrowing an exponentially large set of objects, such as proteins or molecules, to a … (voir plus)small set of promising candidates with desirable properties. While this process can rely on expert knowledge, recent methods leverage reinforcement learning (RL) guided by a proxy reward function to enable this filtering. By employing various forms of entropy regularization, these methods aim to learn samplers that generate diverse candidates that are highly rated by the proxy function. In this work, we make two main contributions. First, we show that these methods are liable to generate overly diverse, suboptimal candidates in large search spaces. To address this issue, we introduce a novel unified operator that combines several regularized RL operators into a general framework that better targets peakier sampling distributions. Secondly, we offer a novel, robust RL perspective of this filtering process. The regularization can be interpreted as robustness to a compositional form of uncertainty in the proxy function (i.e., the true evaluation of a candidate differs from the proxy's evaluation). Our analysis leads us to a novel, easy-to-use algorithm we name trajectory general mellowmax (TGM): we show it identifies higher quality, diverse candidates than baselines in both synthetic and real-world tasks. Code: https://github.com/marcojira/tgm.
2025-05-31
arXiv (publié)
doi.org
arxiv.org
Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis
Jia Lin Hau
Erick Delage
Esther Derman
Mohammad Ghavamzadeh
Marek Petrik
In Markov decision processes (MDPs), quantile risk measures such as Value-at-Risk are a standard metric for modeling RL agents' preferences … (voir plus)for certain outcomes. This paper proposes a new Q-learning algorithm for quantile optimization in MDPs with strong convergence and performance guarantees. The algorithm leverages a new, simple dynamic program (DP) decomposition for quantile MDPs. Compared with prior work, our DP decomposition requires neither known transition probabilities nor solving complex saddle point equations and serves as a suitable foundation for other model-free RL algorithms. Our numerical results in tabular domains show that our Q-learning algorithm converges to its DP variant and outperforms earlier algorithms.
2025-04-22
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press