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Results for "DRL"

Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing

arXiv · May 20

A new research paper systematically benchmarked two learning-based and two empirical feedforward steering controllers for autonomous racing, introducing a new Empirical Hysteresis Dynamics (EHD) formulation. The study utilized a high-fidelity simulation framework based on the real-world Abu Dhabi Autonomous Racing League competition. While learning-based controllers showed lower prediction errors in open-loop evaluation, the proposed EHD approach achieved the best overall closed-loop robustness and lap times. Why it matters: This research highlights the critical importance of evaluating control strategies within a complete software stack for autonomous racing, directly informing the development for competitions like the AADRL.

Learning to Identify Critical States for Reinforcement Learning from Videos

arXiv · Aug 15

Researchers at KAUST have developed a new method called Deep State Identifier for extracting information from videos for reinforcement learning. The method learns to predict returns from video-encoded episodes and identifies critical states using mask-based sensitivity analysis. Experiments demonstrate the method's potential for understanding and improving agent behavior in DRL.

Recent Advances in Deep Reinforcement Learning

MBZUAI · Invalid Date

Keith Ross, Dean of Computer Science, Data Science and Engineering at NYU Shanghai, will be giving a talk on recent advances in Deep Reinforcement Learning (DRL). The talk will review DRL breakthroughs and discuss algorithmic research on DRL for high-dimensional state and action spaces, with applications to robotic locomotion. Ross's research interests include deep reinforcement learning, Internet privacy, peer-to-peer networking, and computer network modeling. Why it matters: Reinforcement learning is a core area of AI research in the GCC region, and a talk by a prominent researcher can help inform and inspire local researchers.