881 citations · 1.7k across the 23 of their papers we have counts for
25 papers
PTDRL: Parameter Tuning using Deep Reinforcement Learning
Elias Goldsztejn, Tal Feiner, Ronen Brafman
A variety of autonomous navigation algorithms exist that allow robots to move around in a safe and fast manner. However, many of these algorithms require parameter re-tuning when f…
Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models
Or Wertheim, Dan R. Suissa, Ronen I. Brafman
To enable robots to achieve high level objectives, engineers typically write scripts that apply existing specialized skills, such as navigation, object detection and manipulation t…
Learning and Solving Regular Decision Processes
Eden Abadi, Ronen I. Brafman
Regular Decision Processes (RDPs) are a recently introduced model that extends MDPs with non-Markovian dynamics and rewards. The non-Markovian behavior is restricted to depend on r…
Reinforcement Learning with Non-Markovian Rewards
Maor Gaon, Ronen I. Brafman
The standard RL world model is that of a Markov Decision Process (MDP). A basic premise of MDPs is that the rewards depend on the last state and action only. Yet, many real-world r…
Privacy Preserving Multi-Agent Planning with Provable Guarantees
Amos Beimel, Ronen I. Brafman
In privacy-preserving multi-agent planning, a group of agents attempt to cooperatively solve a multi-agent planning problem while maintaining private their data and actions. Althou…
A Planning Approach to Monitoring Behavior of Computer Programs
Alexandre Cukier, Ronen I. Brafman, Yotam Perkal +1
We describe a novel approach to monitoring high level behaviors using concepts from AI planning. Our goal is to understand what a program is doing based on its system call trace. T…