Publications de Luca Viano | EPFL Graph Search

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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. ...

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 ...

Luca Viano

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

Best of Both Worlds: Regret Minimization versus Minimax Play

Identifiability and Generalizability from Multiple Experts in Inverse Reinforcement Learning

Proximal Point Imitation Learning

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

Imitation Learning in Discounted Linear MDPs without exploration assumptions

Adaptive Bilevel Optimization

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

Alternation makes the adversary weaker in two-player games

IL-SOAR : Imitation Learning with Soft Optimistic Actor cRitic

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