no code implementations • 26 Mar 2024 • Paula Stocco, Suhas Chundi, Arec Jamgochian, Mykel J. Kochenderfer
Lagrangian-guided Monte Carlo tree search with global dual ascent has been applied to solve large constrained partially observable Markov decision processes (CPOMDPs) online.
1 code implementation • 30 Oct 2023 • Arec Jamgochian, Hugo Buurmeijer, Kyle H. Wray, Anthony Corso, Mykel J. Kochenderfer
Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs) maximize reward objectives while satisfying hard cost constraints, generalizing safe planning under state and transition uncertainty.
1 code implementation • 23 Dec 2022 • Arec Jamgochian, Anthony Corso, Mykel J. Kochenderfer
Rather than augmenting rewards with penalties for undesired behavior, Constrained Partially Observable Markov Decision Processes (CPOMDPs) plan safely by imposing inviolable hard constraint value budgets.
no code implementations • 1 Jun 2022 • Junyoung Park, Federico Berto, Arec Jamgochian, Mykel J. Kochenderfer, Jinkyoo Park
In this paper, we propose Meta-SysId, a meta-learning approach to model sets of systems that have behavior governed by common but unknown laws and that differentiate themselves by their context.
1 code implementation • 8 Jan 2022 • Arec Jamgochian, Di wu, Kunal Menda, Soyeon Jung, Mykel J. Kochenderfer
In this paper, we introduce the conditional approximate normalizing flow (CANF) to make probabilistic multi-step time-series forecasts when correlations are present over long time horizons.