Yonathan Efroni

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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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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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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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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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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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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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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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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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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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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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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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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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Yonathan Efroni

Featured Co-authors

Shie Mannor

Mohammad Ghavamzadeh

Matteo Pirotta

Bruno Scherrer

Gal Dalal

Chen Tessler

Lior Shani

Nadav Merlis

Manan Tomar

Aviv Rosenberg

Beyond the One Step Greedy Approach in Reinforcement Learning

Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning

How to Combine Tree-Search Methods in Reinforcement Learning

Revisiting Exploration-Conscious Reinforcement Learning

Action Robust Reinforcement Learning and Applications in Continuous Control

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

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

Multi-Step Greedy and Approximate Real Time Dynamic Programming

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

Optimistic Policy Optimization with Bandit Feedback

Exploration-Exploitation in Constrained MDPs

Mirror Descent Policy Optimization