107 citations
- Centre National de la Recherche ScientifiqueFR6 papers
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6 papers · 1 filter
Entropy Regularized Reinforcement Learning with Cascading Networks
Riccardo Della Vecchia, Alena Shilova, Philippe Preux +1
Deep Reinforcement Learning (Deep RL) has had incredible achievements on high dimensional problems, yet its learning process remains unstable even on the simplest tasks. Deep RL us…
Soft Action Priors: Towards Robust Policy Transfer
Matheus Centa, Philippe Preux
Despite success in many challenging problems, reinforcement learning (RL) is still confronted with sample inefficiency, which can be mitigated by introducing prior knowledge to age…
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits
Achraf Azize, Debabrota Basu
We study the problem of multi-armed bandits with -global Differential Privacy (DP). First, we prove the minimax and problem-dependent regret lower bounds for stochastic and line…
Optimistic PAC Reinforcement Learning: the Instance-Dependent View
Andrea Tirinzoni, Aymen Al-Marjani, Emilie Kaufmann
Optimistic algorithms have been extensively studied for regret minimization in episodic tabular MDPs, both from a minimax and an instance-dependent view. However, for the PAC RL pr…
gym-DSSAT: a crop model turned into a Reinforcement Learning environment
Romain Gautron, Emilio J. Padrón, Philippe Preux +3
Addressing a real world sequential decision problem with Reinforcement Learning (RL) usually starts with the use of a simulated environment that mimics real conditions. We present…
Efficient Algorithms for Extreme Bandits
Dorian Baudry, Yoan Russac, Emilie Kaufmann
In this paper, we contribute to the Extreme Bandit problem, a variant of Multi-Armed Bandits in which the learner seeks to collect the largest possible reward. We first study the c…