11 citations · 17 across the 14 of their papers we have counts for
14 papers
Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning
Asaf Cassel, Aviv Rosenberg
Optimal Reinforcement Learning (RL) algorithms typically rely on carefully constructed count-based uncertainty estimates to drive exploration. Although theoretically sound, such es…
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits
Asaf Cassel, Orin Levy, Yishay Mansour
Efficiently trading off exploration and exploitation is one of the key challenges in online Reinforcement Learning (RL). Most works achieve this by carefully estimating the model u…
Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes
Asaf Cassel, Aviv Rosenberg
Policy Optimization (PO) methods are among the most popular Reinforcement Learning (RL) algorithms in practice. Recently, Sherman et al. [2023a] proposed a PO-based algorithm with…
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.…
Multi-turn Reinforcement Learning from Preference Human Feedback
Lior Shani, Aviv Rosenberg, Asaf Cassel +10
Reinforcement Learning from Human Feedback (RLHF) has become the standard approach for aligning Large Language Models (LLMs) with human preferences, allowing LLMs to demonstrate re…
Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation
Orin Levy, Alon Cohen, Asaf Cassel +1
We present the OMG-CMDP! algorithm for regret minimization in adversarial Contextual MDPs. The algorithm operates under the minimal assumptions of realizable function class and acc…
Eluder-based Regret for Stochastic Contextual MDPs
Orin Levy, Asaf Cassel, Alon Cohen +1
We present the E-UCRL algorithm for regret minimization in Stochastic Contextual Markov Decision Processes (CMDPs). The algorithm operates under the minimal assumptions of real…
Rate-Optimal Online Convex Optimization in Adaptive Linear Control
Asaf Cassel, Alon Cohen, Tomer Koren
We consider the problem of controlling an unknown linear dynamical system under adversarially changing convex costs and full feedback of both the state and cost function. We presen…
Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics
Asaf Cassel, Alon Cohen, Tomer Koren
We consider the problem of controlling an unknown linear dynamical system under a stochastic convex cost and full feedback of both the state and cost function. We present a computa…
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with Regret
Asaf Cassel, Tomer Koren
We consider the task of learning to control a linear dynamical system under fixed quadratic costs, known as the Linear Quadratic Regulator (LQR) problem. While model-free approache…
The Pendulum Arrangement: Maximizing the Escape Time of Heterogeneous Random Walks
Asaf Cassel, Shie Mannor, Guy Tennenholtz
We identify a fundamental phenomenon of heterogeneous one dimensional random walks: the escape (traversal) time is maximized when the heterogeneity in transition probabilities form…
Bandit Linear Control
Asaf Cassel, Tomer Koren
We consider the problem of controlling a known linear dynamical system under stochastic noise, adversarially chosen costs, and bandit feedback. Unlike the full feedback setting whe…
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently
Asaf Cassel, Alon Cohen, Tomer Koren
We consider the problem of learning in Linear Quadratic Control systems whose transition parameters are initially unknown. Recent results in this setting have demonstrated efficien…
A General Framework for Bandit Problems Beyond Cumulative Objectives
Asaf Cassel, Shie Mannor, Assaf Zeevi
The stochastic multi-armed bandit (MAB) problem is a common model for sequential decision problems. In the standard setup, a decision maker has to choose at every instant between s…