מושגי ליבה
Uni-O4 proposes a seamless transition between offline and online learning, enhancing performance and efficiency in deep reinforcement learning.
תקציר
Uni-O4 introduces an innovative approach to combine offline and online reinforcement learning seamlessly. By leveraging on-policy optimization, the algorithm achieves superior performance in both offline initialization and online fine-tuning. The method addresses challenges of conservatism and policy constraints, demonstrating remarkable efficiency in real-world robot tasks.
סטטיסטיקה
Published as a conference paper at ICLR 2024
Shanghai Qi Zhi Institute, Tsinghua University, IIIS, Shanghai AI Lab, The Hong Kong University of Science and Technology (Guangzhou)
Email contacts provided: leikun980116@gmail.com, huazhe_xu@mail.tsinghua.edu.cn
Various simulated benchmarks used for evaluation
ציטוטים
"Combining offline and online reinforcement learning is crucial for efficient and safe learning."
"We propose Uni-O4, which utilizes an on-policy objective for both offline and online learning."
"Uni-O4 significantly enhances the offline performance compared to BPPO without the need for online evaluation."