Publications by Luca Viano | EPFL Graph Search

IL-SOAR : Imitation Learning with Soft Optimistic Actor cRitic

This paper introduces the SOAR framework for imitation learning. SOAR is an algorithmic template that learns a policy from expert demonstrations with a primal dual style algorithm that alternates cost and policy updates. Within the policy updates, the SOAR ...

2025

Best of Both Worlds: Regret Minimization versus Minimax Play

Volkan Cevher, Luca Viano

In this paper, we investigate the existence of online learning algorithms with bandit feedback that simultaneously guarantee O(1) regret compared to a given comparator strategy, and Õ(√ T) regret compared to any fixed strategy, where T is the number of rou ...

2025

Adaptive Bilevel Optimization

Volkan Cevher, Luca Viano, Kimon Antonakopoulos

We propose a new adaptive optimization algorithm based on mirror descent for a class of possibly non-convex smooth bilevel optimization problems. The bilevel optimization template is broadly applicable in machine learning as it features two coupled problem ...

Association for Computing Machinery (ACM)2025

Imitation Learning in Discounted Linear MDPs without exploration assumptions

Volkan Cevher, Luca Viano, Efstratios Panteleimon Skoulakis

We present a new algorithm for imitation learning in infinite horizon linear MDPs dubbed ILARL which greatly improves the bound on the number of trajectories that the learner needs to sample from the environment. In particular, we re- move exploration assu ...

2024

Alternation makes the adversary weaker in two-player games

Volkan Cevher, Ali Kavis, Luca Viano, Efstratios Panteleimon Skoulakis

Motivated by alternating game-play in two-player games, we study an altenating variant of the Online Linear Optimization (OLO). In alternating OLO, a learner at each round t ∈[n] selects a vector xt and then an adversary selects a cost-vector ct ∈[−1,1]n. ...

2023

What can online reinforcement learning with function approximation benefitfrom general coverage conditions

Volkan Cevher, Luca Viano, Fanghui Liu

2023

Semi Bandit Dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees

Volkan Cevher, Luca Viano, Efstratios Panteleimon Skoulakis

In this work, we introduce a new variant of online gradient descent, which provably converges to Nash Equilibria and simultaneously attains sublinear regret for the class of congestion games in the semi-bandit feedback setting. Our proposed method admits c ...

2023

A Natural Actor-Critic Framework for Zero-Sum Markov Games

Volkan Cevher, Luca Viano

We introduce algorithms based on natural actorcritic and analyze their sample complexity for solving two player zero-sum Markov games in the tabular case. Our results improve the best-known sample complexities of policy gradient/actorcritic methods for con ...

PMLR2022

Understanding Deep Neural Function Approximation in Reinforcement Learning via ϵ-Greedy Exploration

Volkan Cevher, Luca Viano, Fanghui Liu

This paper provides a theoretical study of deep neural function approximation in reinforcement learning (RL) with the ϵ-greedy exploration under the online setting. This problem setting is motivated by the successful deep Q-networks (DQN) framework that fa ...

2022

Proximal Point Imitation Learning

Volkan Cevher, Angeliki Kamoutsi, Igor Krawczuk, Luca Viano

This work develops new algorithms with rigorous efficiency guarantees for infinite horizon imitation learning (IL) with linear function approximation without restrictive coherence assumptions. We begin with the minimax formulation of the problem and then o ...

2022

Identifiability and Generalizability from Multiple Experts in Inverse Reinforcement Learning

Boris Nikolov, Volkan Cevher, Paul Thierry Yves Rolland, Luca Viano

While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert’s behavior. It is well known that, in general, variou ...

2022

Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch

Volkan Cevher, Kamalaruban Parameswaran, Luca Viano, Yu-Ting Huang

We study the inverse reinforcement learning (IRL) problem under a transition dynamics mismatch between the expert and the learner. Specifically, we consider the Maximum Causal Entropy (MCE) IRL learner model and provide a tight upper bound on the learner’s ...

2021