1 citations · 2 across the 5 of their papers we have counts for
6 papers
Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead
Uri Koren, Navdeep Kumar, Uri Gadot +3
Classical policy gradient (PG) methods in reinforcement learning frequently converge to suboptimal local optima, a challenge exacerbated in large or complex environments. This work…
Policy Optimized Text-to-Image Pipeline Design
Uri Gadot, Rinon Gal, Yftah Ziser +2
Text-to-image generation has evolved beyond single monolithic models to complex multi-component pipelines. These combine fine-tuned generators, adapters, upscaling blocks and even…
Simulus: Combining Improvements in Sample-Efficient World Model Agents
Lior Cohen, Kaixin Wang, Bingyi Kang +2
World models (WMs) represent the frontier of sample-efficient reinforcement learning, but their complexity leaves many promising improvements unrealized due to the significant expe…
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression
Uri Gadot, Assaf Shocher, Shie Mannor +2
Video encoders optimize compression for human perception by minimizing reconstruction error under bit-rate constraints. In many modern applications such as autonomous driving, an o…
Solving Non-Rectangular Reward-Robust MDPs via Frequency Regularization
Uri Gadot, Esther Derman, Navdeep Kumar +3
In robust Markov decision processes (RMDPs), it is assumed that the reward and the transition dynamics lie in a given uncertainty set. By targeting maximal return under the most ad…
Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel
Kaixin Wang, Uri Gadot, Navdeep Kumar +2
Robust Markov Decision Processes (RMDPs) provide a framework for sequential decision-making that is robust to perturbations on the transition kernel. However, current RMDP methods…