USTC’s MIRA Lab Tackles Robust Offline Reinforcement Learning with TRACER, Accepted to NeurIPS 2024
A novel approach addressesthe challenge of multi-faceted data corruption in offline reinforcement learning datasets, paving the way for more robust AI systems in robotics and autonomous driving.
Offline reinforcement learning(RL) is rapidly emerging as a crucial technology for enhancing decision-making and control capabilities in robotics. However, real-world offline datasets are frequently corrupteddue to various factors, including sensor malfunctions, adversarial attacks, and other forms of data perturbation. This corruption, manifesting as random noise, adversarial attacks, or other data distortions affecting states and actions, significantly hinders the performance and reliability of RL agents. Addressing this issue is critical for deploying robust AI systems in safety-critical applications.
This challenge has been effectively tackled by a team from the University of Science and Technology of China (USTC)’s MIRA Lab, led byProfessor Jie Wang. Their research, culminating in the TRACER algorithm, has been accepted to NeurIPS 2024, a leading conference in artificial intelligence. TRACER introduces a robust variational Bayesian inference method specifically designed to handle multiple types of data corruption simultaneously – a significant advancement over existing methods that typically address onlysingle corruption types.
The core innovation lies in TRACER’s ability to effectively disentangle the effects of different corruption sources within the offline dataset. This allows the algorithm to learn a more accurate and robust policy, even in the presence of significant data noise and adversarial influences. The researchers’ approach significantly improves therobustness of intelligent decision-making models, laying a crucial foundation for robust learning in fields such as robotics control and autonomous driving.
Yang Rui, a 2019 joint master’s and doctoral student at USTC under the supervision of Professors Jie Wang and Bin Li, is the first author of the paper.His research focuses on reinforcement learning and autonomous driving. He has previously published two first-author papers in top-tier journals and conferences, including NeurIPS and KDD, and was recognized as a Didi Elite Intern (16 out of 1000+).
The TRACER algorithm offers a promisingsolution to a critical bottleneck in the practical application of offline RL. Its ability to handle diverse data corruption types makes it particularly suitable for real-world scenarios where data quality is often compromised. The team’s work represents a substantial contribution to the field, pushing the boundaries of robust AI and paving the way for saferand more reliable intelligent systems.
Conclusion:
The acceptance of TRACER at NeurIPS 2024 underscores the significance of this research. The algorithm’s ability to address multi-faceted data corruption in offline RL datasets represents a major step forward in developing robust and reliable AI systems for applications like roboticsand autonomous driving. Future research could focus on extending TRACER’s capabilities to even more complex corruption scenarios and exploring its application in other domains requiring robust decision-making under uncertainty.
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