45 citations · 70 across the 17 of their papers we have counts for
16 papers · 1 filter
Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation
Orin Levy, Aviv Rosenberg, Alon Cohen +1
We introduce \texttt{OPO-CMDP}, the first policy optimization algorithm for stochastic Contextual Markov Decision Process (CMDPs) under general offline function approximation. Our…
Online Weighted Paging with Unknown Weights
Orin Levy, Noam Touitou, Aviv Rosenberg
Online paging is a fundamental problem in the field of online algorithms, in which one maintains a cache of slots as requests for fetching pages arrive online. In the weighted…
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback
Asaf Cassel, Haipeng Luo, Aviv Rosenberg +1
In many real-world applications, it is hard to provide a reward signal in each step of a Reinforcement Learning (RL) process and more natural to give feedback when an episode ends.…
A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs
Dirk van der Hoeven, Lukas Zierahn, Tal Lancewicki +2
We derive a new analysis of Follow The Regularized Leader (FTRL) for online learning with delayed bandit feedback. By separating the cost of delayed feedback from that of bandit fe…
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback
Tal Lancewicki, Aviv Rosenberg, Dmitry Sotnikov
Policy Optimization (PO) is one of the most popular methods in Reinforcement Learning (RL). Thus, theoretical guarantees for PO algorithms have become especially important to the R…
Policy Optimization for Stochastic Shortest Path
Liyu Chen, Haipeng Luo, Aviv Rosenberg
Policy optimization is among the most popular and successful reinforcement learning algorithms, and there is increasing interest in understanding its theoretical guarantees. In thi…
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback
Tiancheng Jin, Tal Lancewicki, Haipeng Luo +2
The standard assumption in reinforcement learning (RL) is that agents observe feedback for their actions immediately. However, in practice feedback is often observed in delay. This…
Cooperative Online Learning in Stochastic and Adversarial MDPs
Tal Lancewicki, Aviv Rosenberg, Yishay Mansour
We study cooperative online learning in stochastic and adversarial Markov decision process (MDP). That is, in each episode, agents interact with an MDP simultaneously and share…
Planning and Learning with Adaptive Lookahead
Aviv Rosenberg, Assaf Hallak, Shie Mannor +2
Some of the most powerful reinforcement learning frameworks use planning for action selection. Interestingly, their planning horizon is either fixed or determined arbitrarily by th…
Minimax Regret for Stochastic Shortest Path
Alon Cohen, Yonathan Efroni, Yishay Mansour +1
We study the Stochastic Shortest Path (SSP) problem in which an agent has to reach a goal state in minimum total expected cost. In the learning formulation of the problem, the agen…
Learning Adversarial Markov Decision Processes with Delayed Feedback
Tal Lancewicki, Aviv Rosenberg, Yishay Mansour
Reinforcement learning typically assumes that agents observe feedback for their actions immediately, but in many real-world applications (like recommendation systems) feedback is o…
Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure
Aviv Rosenberg, Yishay Mansour
We study regret minimization in non-episodic factored Markov decision processes (FMDPs), where all existing algorithms make the strong assumption that the factored structure of the…
Stochastic Shortest Path with Adversarially Changing Costs
Aviv Rosenberg, Yishay Mansour
Stochastic shortest path (SSP) is a well-known problem in planning and control, in which an agent has to reach a goal state in minimum total expected cost. In this paper we present…
Near-optimal Regret Bounds for Stochastic Shortest Path
Alon Cohen, Haim Kaplan, Yishay Mansour +1
Stochastic shortest path (SSP) is a well-known problem in planning and control, in which an agent has to reach a goal state in minimum total expected cost. In the learning formulat…
Optimistic Policy Optimization with Bandit Feedback
Yonathan Efroni, Lior Shani, Aviv Rosenberg +1
Policy optimization methods are one of the most widely used classes of Reinforcement Learning (RL) algorithms. Yet, so far, such methods have been mostly analyzed from an optimizat…
Online Convex Optimization in Adversarial Markov Decision Processes
Aviv Rosenberg, Yishay Mansour
We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss function can change arbitrarily between episodes, and the transition function is…