3 papers
cs.RO2024
Towards Natural Language-Driven Assembly Using Foundation Models
Omkar Joglekar, Tal Lancewicki, Shir Kozlovsky +3
Large Language Models (LLMs) and strong vision models have enabled rapid research and development in the field of Vision-Language-Action models that enable robotic control. The mai…
cs.LG2023
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…
cs.LG2023
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…