2 citations · 2 across the 10 of their papers we have counts for
11 papers
Generating Instance Generators in PDDL Planning
Nicola J. Müller, Naya Rudolph, Katharina Stein +3
PDDL, the de-facto standard language in the AI Planning community, is designed to specify planning domains: sets of instances that share the same predicates and action schemas. Yet…
Model-Driven Policy Optimization in Differentiable Simulators via Stochastic Exploration
Yuval Aroosh, Ayal Taitler
Differentiable planning enables gradient-based optimization of decision-making problems by leveraging differentiable models of system dynamics. However, in highly nonlinear and hyb…
From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution
Lidor Erez, Shahaf S. Shperberg, Ayal Taitler
In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission. Such problems are inherently mixed discrete-continuous: a high-level action sequenc…
Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers
Xiaoyu Wang, Ayal Taitler, Scott Sanner +1
Efficient traffic signal control is essential for managing urban transportation, minimizing congestion, and improving safety and sustainability. Reinforcement Learning (RL) has eme…
A Sequential Decision-Making Model for Perimeter Identification
Ayal Taitler
Perimeter identification involves ascertaining the boundaries of a designated area or zone, requiring traffic flow monitoring, control, or optimization. Various methodologies and t…
Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs
Michael Gimelfarb, Ayal Taitler, Scott Sanner
We propose the Constraint-Generation Policy Optimization (CGPO) framework to optimize policy parameters within compact and interpretable policy classes for mixed discrete-continuou…
Perimeter Control Using Deep Reinforcement Learning: A Model-free Approach towards Homogeneous Flow Rate Optimization
Xiaocan Li, Ray Coden Mercurius, Ayal Taitler +4
Perimeter control maintains high traffic efficiency within protected regions by controlling transfer flows among regions to ensure that their traffic densities are below critical v…
A2D: Anywhere Anytime Drumming
Harel Yadid, Almog Algranti, Mark Levin +1
The drum kit, which has only been around for around 100 years, is a popular instrument in many music genres such as pop, rock, and jazz. However, the road to owning a kit is expens…
pyRDDLGym: From RDDL to Gym Environments
Ayal Taitler, Michael Gimelfarb, Jihwan Jeong +4
We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym environments from RDDL declerative description. The discrete time step evolution of variables in RDDL is…
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems
Joel Oren, Chana Ross, Maksym Lefarov +5
We study combinatorial problems with real world applications such as machine scheduling, routing, and assignment. We propose a method that combines Reinforcement Learning (RL) and…
Learning Control for Air Hockey Striking using Deep Reinforcement Learning
Ayal Taitler, Nahum Shimkin
We consider the task of learning control policies for a robotic mechanism striking a puck in an air hockey game. The control signal is a direct command to the robot's motors. We em…