Multi Timestep

Multi-timestep modeling addresses the limitations of single-timestep approaches by incorporating information from multiple points in time, improving prediction accuracy and robustness. Current research focuses on developing models that effectively handle varying time horizons, including weighted multi-timestep objectives for reinforcement learning and dynamic routing architectures for real-time perception in applications like autonomous driving. These advancements are significant because they enable more accurate predictions and better decision-making in complex, dynamic systems, particularly those with noisy data or unpredictable delays. The resulting improvements have implications for various fields, including robotics, autonomous systems, and behavioral analysis.

Papers

October 9, 2023

Multi-timestep models for Model-based Reinforcement Learning
Abdelhakim Benechehab, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl
Model Based Reinforcement Learning Batch Reinforcement Learning Step to Step Dynamic Multi Timestep

September 13, 2023

MTD: Multi-Timestep Detector for Delayed Streaming Perception
Yihui Huang, Ningjiang Chen
Autonomous Driving Multi Horizon Streaming Perception Multi Timestep Delay Attribution

June 14, 2022

Learning Behavior Representations Through Multi-Timescale Bootstrapping
Mehdi Azabou, Michael Mendelson, Maks Sorokin, Shantanu Thakoor, Nauman Ahad, Carolina Urzay, Eva L. Dyer
Multi Scale Temporal Dynamic Multi Agent Challenge Animal Behavior Temporal Receptive Field Behavior Representation Multi Timestep

Multi Timestep

Papers

Multi-timestep models for Model-based Reinforcement Learning

MTD: Multi-Timestep Detector for Delayed Streaming Perception

Learning Behavior Representations Through Multi-Timescale Bootstrapping