40 citations · 51 across the 8 of their papers we have counts for
6 papers · 1 filter
ONNXPruner: ONNX-Based General Model Pruning Adapter
Dongdong Ren, Wenbin Li, Tianyu Ding +5
Recent advancements in model pruning have focused on developing new algorithms and improving upon benchmarks. However, the practical application of these algorithms across various…
Decision Transformer under Random Frame Dropping
Kaizhe Hu, Ray Chen Zheng, Yang Gao +1
Controlling agents remotely with deep reinforcement learning~(DRL) in the real world is yet to come. One crucial stepping stone is to devise RL algorithms that are robust in the fa…
Keeping Minimal Experience to Achieve Efficient Interpretable Policy Distillation
Xiao Liu, Shuyang Liu, Wenbin Li +2
Although deep reinforcement learning has become a universal solution for complex control tasks, its real-world applicability is still limited because lacking security guarantees fo…
Mastering Atari Games with Limited Data
Weirui Ye, Shaohuai Liu, Thanard Kurutach +2
Reinforcement learning has achieved great success in many applications. However, sample efficiency remains a key challenge, with prominent methods requiring millions (or even billi…
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning
Bernie Wang, Simon Xu, Kurt Keutzer +2
Meta-reinforcement learning typically requires orders of magnitude more samples than single task reinforcement learning methods. This is because meta-training needs to deal with mo…
Reinforcement Learning with Latent Flow
Wenling Shang, Xiaofei Wang, Aravind Srinivas +4
Temporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such informati…