Timezone: »
Synthesizing optimal controllers for dynamical systems in practice involves solving real-time optimization problems with hard time constraints. These constraints restrict the class of numerical methods that can be applied; indeed, computationally expensive but accurate numerical routines often have to be replaced with fast and inaccurate methods, trading inference time for worse theoretical guarantees on solution accuracy. This paper proposes a novel methodology to accelerate numerical optimization of optimal control policies via hypersolvers, hybrids of a base solver and a neural network. In particular, we apply low–order explicit numerical methods for the ordinary differential equation (ODE) associated to the numerical optimal control problem, augmented with an additional parametric approximator trained to reduce local truncation errors introduced by the base solver. Given a target system to control, we first pre-train hypersolvers to approximate base solver residuals by sampling plausible control inputs. Then, we use the trained hypersolver to obtain fast and accurate solutions of the target system during optimization of the controller. The performance of our approach is evaluated in direct and model predictive optimal control settings, where we show consistent Pareto improvements in terms of solution accuracy and control performance.
Author Information
Federico Berto (Korea Advanced Institute of Science and Technology)
Stefano Massaroli (The University of Tokyo)
Michael Poli (Stanford University)
My work spans topics in deep learning, dynamical systems, variational inference and numerical methods. I am broadly interested in ensuring the successes achieved by deep learning methods in computer vision and natural language are extended to other engineering domains.
Jinkyoo Park (KAIST)
More from the Same Authors
-
2022 : Scale-conditioned Adaptation for Large Scale Combinatorial Optimization »
Minsu Kim · Jiwoo SON · Hyeonah Kim · Jinkyoo Park -
2022 : Collaborative symmetricity exploitation for offline learning of hardware design solver »
HAEYEON KIM · Minsu Kim · joungho kim · Jinkyoo Park -
2022 : Neural Coarsening Process for Multi-level Graph Combinatorial Optimization »
Hyeonah Kim · Minsu Kim · Changhyun Kwon · Jinkyoo Park -
2022 Workshop: The Symbiosis of Deep Learning and Differential Equations II »
Michael Poli · Winnie Xu · Estefany Kelly Buchanan · Maryam Hosseini · Luca Celotti · Martin Magill · Ermal Rrapaj · Qiyao Wei · Stefano Massaroli · Patrick Kidger · Archis Joglekar · Animesh Garg · David Duvenaud -
2022 Poster: Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization »
Minsu Kim · Junyoung Park · Jinkyoo Park -
2022 Poster: Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning »
HYUNWOOK KANG · Taehwan Kwon · Jinkyoo Park · James R. Morrison -
2022 Poster: Transform Once: Efficient Operator Learning in Frequency Domain »
Michael Poli · Stefano Massaroli · Federico Berto · Jinkyoo Park · Tri Dao · Christopher Ré · Stefano Ermon -
2021 : TorchDyn: Implicit Models and Neural Numerical Methods in PyTorch »
Michael Poli · Stefano Massaroli · Atsushi Yamashita · Hajime Asama · Jinkyoo Park · Stefano Ermon -
2021 Workshop: The Symbiosis of Deep Learning and Differential Equations »
Luca Celotti · Kelly Buchanan · Jorge Ortiz · Patrick Kidger · Stefano Massaroli · Michael Poli · Lily Hu · Ermal Rrapaj · Martin Magill · Thorsteinn Jonsson · Animesh Garg · Murtadha Aldeer -
2021 Poster: Differentiable Multiple Shooting Layers »
Stefano Massaroli · Michael Poli · Sho Sonoda · Taiji Suzuki · Jinkyoo Park · Atsushi Yamashita · Hajime Asama -
2021 Poster: Learning Collaborative Policies to Solve NP-hard Routing Problems »
Minsu Kim · Jinkyoo Park · joungho kim -
2021 Poster: Neural Hybrid Automata: Learning Dynamics With Multiple Modes and Stochastic Transitions »
Michael Poli · Stefano Massaroli · Luca Scimeca · Sanghyuk Chun · Seong Joon Oh · Atsushi Yamashita · Hajime Asama · Jinkyoo Park · Animesh Garg -
2020 Poster: Dissecting Neural ODEs »
Stefano Massaroli · Michael Poli · Jinkyoo Park · Atsushi Yamashita · Hajime Asama -
2020 Poster: Hypersolvers: Toward Fast Continuous-Depth Models »
Michael Poli · Stefano Massaroli · Atsushi Yamashita · Hajime Asama · Jinkyoo Park -
2020 Oral: Dissecting Neural ODEs »
Stefano Massaroli · Michael Poli · Jinkyoo Park · Atsushi Yamashita · Hajime Asama