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Aneri Muni

PhD - Université de Montréal
Supervisor
Pierre-Luc Bacon
Research Topics
Reinforcement Learning

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… (see more)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 (preprint)
arxiv.org
arxiv.org
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… (see more)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.
2025-06-10
ICML.cc/2025/Workshop/MoFA (poster)
openreview.net
openreview.net
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization
Leo Feng
Padideh Nouri
Aneri Muni
Yoshua Bengio
Pierre-Luc Bacon
The ability to accelerate the design of biological sequences can have a substantial impact on the progress of the medical field. The problem… (see more) can be framed as a global optimization problem where the objective is an expensive black-box function such that we can query large batches restricted with a limitation of a low number of rounds. Bayesian Optimization is a principled method for tackling this problem. However, the astronomically large state space of biological sequences renders brute-force iterating over all possible sequences infeasible. In this paper, we propose MetaRLBO where we train an autoregressive generative model via Meta-Reinforcement Learning to propose promising sequences for selection via Bayesian Optimization. We pose this problem as that of finding an optimal policy over a distribution of MDPs induced by sampling subsets of the data acquired in the previous rounds. Our in-silico experiments show that meta-learning over such ensembles provides robustness against reward misspecification and achieves competitive results compared to existing strong baselines.
2022-09-13
ArXiv (preprint)
doi.org
arxiv.org