13 citations · 42 across the 7 of their papers we have counts for
9 papers
Safe Reinforcement Learning by Imagining the Near Future
Garrett Thomas, Yuping Luo, Tengyu Ma
Safe reinforcement learning is a promising path toward applying reinforcement learning algorithms to real-world problems, where suboptimal behaviors may lead to actual negative con…
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations
Yuping Luo, Tengyu Ma
Training-time safety violations have been a major concern when we deploy reinforcement learning algorithms in the real world. This paper explores the possibility of safe RL algorit…
Towards Learning to Play Piano with Dexterous Hands and Touch
Huazhe Xu, Yuping Luo, Shaoxiong Wang +2
The virtuoso plays the piano with passion, poetry and extraordinary technical ability. As Liszt said (a virtuoso)must call up scent and blossom, and breathe the breath of life. The…
Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning
Zhiyuan Li, Yuping Luo, Kaifeng Lyu
Matrix factorization is a simple and natural test-bed to investigate the implicit regularization of gradient descent. Gunasekar et al. (2017) conjectured that Gradient Flow with in…
Provable Representation Learning for Imitation Learning via Bi-level Optimization
Sanjeev Arora, Simon S. Du, Sham Kakade +2
A common strategy in modern learning systems is to learn a representation that is useful for many tasks, a.k.a. representation learning. We study this strategy in the imitation lea…
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling
Yuping Luo, Huazhe Xu, Tengyu Ma
Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently. However, learning from demonstrations…