35 citations · 36 across the 2 of their papers we have counts for
2 papers
cs.LG2019★ 1 cited
On the Expressivity of Neural Networks for Deep Reinforcement Learning
Kefan Dong, Yuping Luo, Tengyu Ma
We compare the model-free reinforcement learning with the model-based approaches through the lens of the expressive power of neural networks for policies, -functions, and dynami…
cs.LG2019★ 35 cited
Provably Efficient -learning with Function Approximation via Distribution Shift Error Checking Oracle
Simon S. Du, Yuping Luo, Ruosong Wang +1
-learning with function approximation is one of the most popular methods in reinforcement learning. Though the idea of using function approximation was proposed at least 60 year…