8 citations · 9 across the 3 of their papers we have counts for
4 papers
Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization
Tanmay Ambadkar, Sourav Panda, Shreyash Kale +2
Multi-objective reinforcement learning (MORL) seeks to train agents capable of balancing conflicting objectives. While single preference-conditioned policies offer a highly scalabl…
Imitation-Projected Programmatic Reinforcement Learning
Abhinav Verma, Hoang M. Le, Yisong Yue +1
We study the problem of programmatic reinforcement learning, in which policies are represented as short programs in a symbolic language. Programmatic policies can be more interpret…
Strong NP-hardness of AC power flows feasibility
Daniel Bienstock, Abhinav Verma
We present a rigorous proof of strong NP-hardness of the AC-OPF problem.
The N-K Problem in Power Grids: New Models, Formulations and Numerical Experiments (extended version)
Daniel Bienstock, Abhinav Verma
Given a power grid modeled by a network together with equations describing the power flows, power generation and consumption, and the laws of physics, the so-called N-k problem ask…