Yonathan Efroni

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Minimax Regret for Stochastic Shortest Path

We study the Stochastic Shortest Path (SSP) problem in which an agent ha...
03/24/2021 ∙ by Alon Cohen, et al. ∙ 15 ∙ share

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RL for Latent MDPs: Regret Guarantees and a Lower Bound

In this work, we consider the regret minimization problem for reinforcem...
02/09/2021 ∙ by Jeongyeol Kwon, et al. ∙ 0 ∙ share

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Reinforcement Learning with Trajectory Feedback

The computational model of reinforcement learning is based upon the abil...
08/13/2020 ∙ by Yonathan Efroni, et al. ∙ 46 ∙ share

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Bandits with Partially Observable Offline Data

We study linear contextual bandits with access to a large, partially obs...
06/11/2020 ∙ by Guy Tennenholtz, et al. ∙ 0 ∙ share

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Mirror Descent Policy Optimization

We propose deep Reinforcement Learning (RL) algorithms inspired by mirro...
05/20/2020 ∙ by Manan Tomar, et al. ∙ 0 ∙ share

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Exploration-Exploitation in Constrained MDPs

In many sequential decision-making problems, the goal is to optimize a u...
03/04/2020 ∙ by Yonathan Efroni, et al. ∙ 0 ∙ share

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Optimistic Policy Optimization with Bandit Feedback

Policy optimization methods are one of the most widely used classes of R...
02/19/2020 ∙ by Yonathan Efroni, et al. ∙ 0 ∙ share

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Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning

Multi-step greedy policies have been extensively used in model-based Rei...
10/07/2019 ∙ by Manan Tomar, et al. ∙ 0 ∙ share

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Multi-Step Greedy and Approximate Real Time Dynamic Programming

Real Time Dynamic Programming (RTDP) is a well-known Dynamic Programming...
09/10/2019 ∙ by Yonathan Efroni, et al. ∙ 0 ∙ share

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Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs

Trust region policy optimization (TRPO) is a popular and empirically suc...
09/06/2019 ∙ by Lior Shani, et al. ∙ 0 ∙ share

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Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies

State-of-the-art efficient model-based Reinforcement Learning (RL) algor...
05/27/2019 ∙ by Yonathan Efroni, et al. ∙ 0 ∙ share

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Action Robust Reinforcement Learning and Applications in Continuous Control

A policy is said to be robust if it maximizes the reward while consideri...
01/26/2019 ∙ by Chen Tessler, et al. ∙ 0 ∙ share

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Revisiting Exploration-Conscious Reinforcement Learning

The objective of Reinforcement Learning is to learn an optimal policy by...
12/13/2018 ∙ by Lior Shani, et al. ∙ 0 ∙ share

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How to Combine Tree-Search Methods in Reinforcement Learning

Finite-horizon lookahead policies are abundantly used in Reinforcement L...
09/06/2018 ∙ by Yonathan Efroni, et al. ∙ 0 ∙ share

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Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning

Multiple-step lookahead policies have demonstrated high empirical compet...
05/21/2018 ∙ by Yonathan Efroni, et al. ∙ 0 ∙ share

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Beyond the One Step Greedy Approach in Reinforcement Learning

The famous Policy Iteration algorithm alternates between policy improvem...
02/10/2018 ∙ by Yonathan Efroni, et al. ∙ 0 ∙ share

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Yonathan Efroni

Featured Co-authors

Shie Mannor

Yishay Mansour

Mohammad Ghavamzadeh

Constantine Caramanis

Matteo Pirotta

Uri Shalit

Bruno Scherrer

Gal Dalal

Alon Cohen

Guy Tennenholtz

Chen Tessler

Minimax Regret for Stochastic Shortest Path

RL for Latent MDPs: Regret Guarantees and a Lower Bound

Reinforcement Learning with Trajectory Feedback

Bandits with Partially Observable Offline Data

Mirror Descent Policy Optimization

Exploration-Exploitation in Constrained MDPs

Optimistic Policy Optimization with Bandit Feedback

Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning

Multi-Step Greedy and Approximate Real Time Dynamic Programming

Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs

Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies

Action Robust Reinforcement Learning and Applications in Continuous Control

Revisiting Exploration-Conscious Reinforcement Learning

How to Combine Tree-Search Methods in Reinforcement Learning

Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning

Beyond the One Step Greedy Approach in Reinforcement Learning