Cover Image

Multistep Inverse Is Not All You Need

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Levine, Alexander, Stone, Peter, Zhang, Amy
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Systems and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Belief Samples Are All You Need For Social Learning
by: JafariNodeh, Mahyar, et al.
Published: (2024)

Data is All You Need: Markov Chain Car-Following (MC-CF) Model
by: Chung, Sungyong, et al.
Published: (2026)

Learning Passive Continuous-Time Dynamics with Multistep Port-Hamiltonian Gaussian Processes
by: Leung, Chi Ho, et al.
Published: (2025)

Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems
by: Chen, Lingjiao, et al.
Published: (2024)

Architecture Is All You Need: Diversity-Enabled Sweet Spots for Robust Humanoid Locomotion
by: Werner, Blake, et al.
Published: (2025)

Scalable Kernel Inverse Optimization
by: Long, Youyuan, et al.
Published: (2024)

Multistep Criticality Search and Power Shaping in Microreactors with Reinforcement Learning
by: Radaideh, Majdi I., et al.
Published: (2024)

Offline Reinforcement Learning via Inverse Optimization
by: Dimanidis, Ioannis, et al.
Published: (2025)

Behavioral Exploration: Learning to Explore via In-Context Adaptation
by: Wagenmaker, Andrew, et al.
Published: (2025)

Offline Hierarchical Reinforcement Learning via Inverse Optimization
by: Schmidt, Carolin, et al.
Published: (2024)

Robustly Invertible Nonlinear Dynamics and the BiLipREN: Contracting Neural Models with Contracting Inverses
by: Zhang, Yurui, et al.
Published: (2025)

Can Optimal Transport Improve Federated Inverse Reinforcement Learning?
by: Millard, David, et al.
Published: (2026)

Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory
by: Levine, Alexander, et al.
Published: (2024)

Offline Action-Free Learning of Ex-BMDPs by Comparing Diverse Datasets
by: Levine, Alexander, et al.
Published: (2025)

Stable Inverse Reinforcement Learning: Policies from Control Lyapunov Landscapes
by: Tesfazgi, Samuel, et al.
Published: (2024)

Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning
by: Town, Jared, et al.
Published: (2022)

Attention is All You Need to Optimize Wind Farm Operations and Maintenance
by: Kazemian, Iman, et al.
Published: (2024)

Inverse Particle Filter
by: Singh, Himali, et al.
Published: (2024)

First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling
by: Davydov, Alexander, et al.
Published: (2024)

Generative Model Predictive Control in Manufacturing Processes: A Review
by: Lee, Suk Ki, et al.
Published: (2025)

Inverse Reinforcement Learning for Minimum-Exposure Paths in Spatiotemporally Varying Scalar Fields
by: Ballentine, Alexandra E., et al.
Published: (2025)

Joint Age-State Belief is All You Need: Minimizing AoII via Pull-Based Remote Estimation
by: Cosandal, Ismail, et al.
Published: (2024)

Inverse Unscented Kalman Filter
by: Singh, Himali, et al.
Published: (2023)

Canonical Bayesian Linear System Identification
by: Bryutkin, Andrey, et al.
Published: (2025)

Inverse Cubature and Quadrature Kalman filters
by: Singh, Himali, et al.
Published: (2023)

One Model to Forecast Them All and in Entity Distributions Bind Them
by: Bölat, Kutay, et al.
Published: (2025)

Inverse Delayed Reinforcement Learning
by: Zhan, Simon Sinong, et al.
Published: (2024)

Pricing is All You Need to Improve Traffic Routing
by: Tang, Yu, et al.
Published: (2025)

Why Change Your Controller When You Can Change Your Planner: Drag-Aware Trajectory Generation for Quadrotor Systems
by: Zhang, Hanli, et al.
Published: (2024)

Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?
by: Wu, Xu, et al.
Published: (2025)

Prepare Before You Act: Learning From Humans to Rearrange Initial States
by: Dai, Yinlong, et al.
Published: (2025)

Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioning
by: Davydov, Alexander
Published: (2025)

Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
by: Schulze, Jan C., et al.
Published: (2024)

All You Need is an Improving Column: Enhancing Column Generation for Parallel Machine Scheduling via Transformers
by: Hijazi, Amira, et al.
Published: (2024)

Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces
by: Mouton, Valentin, et al.
Published: (2025)

Transfer of Safety Controllers Through Learning Deep Inverse Dynamics Model
by: Nadali, Alireza, et al.
Published: (2024)

Inverse Design in Distributed Circuits Using Single-Step Reinforcement Learning
by: Li, Jiayu, et al.
Published: (2025)

Convex and Non-convex Federated Learning with Stale Stochastic Gradients: Diminishing Step Size is All You Need
by: Zheng, Xinran, et al.
Published: (2026)

Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining
by: Hose, Henrik, et al.
Published: (2024)

Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning
by: Lin, Runze, et al.
Published: (2025)