54 citations · 217 across the 12 of their papers we have counts for
10 papers · 1 filter
On the Generalization of Representations in Reinforcement Learning
Charline Le Lan, Stephen Tu, Adam Oberman +2
In reinforcement learning, state representations are used to tractably deal with large problem spaces. State representations serve both to approximate the value function with few p…
Regret Bounds for Adaptive Nonlinear Control
Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
We study the problem of adaptively controlling a known discrete-time nonlinear system subject to unmodeled disturbances. We prove the first finite-time regret bounds for adaptive n…
Learning Stability Certificates from Data
Nicholas M. Boffi, Stephen Tu, Nikolai Matni +2
Many existing tools in nonlinear control theory for establishing stability or safety of a dynamical system can be distilled to the construction of a certificate function that guara…
Observational Overfitting in Reinforcement Learning
Xingyou Song, Yiding Jiang, Stephen Tu +2
A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the…
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator
Karl Krauth, Stephen Tu, Benjamin Recht
We study the sample complexity of approximate policy iteration (PI) for the Linear Quadratic Regulator (LQR), building on a recent line of work using LQR as a testbed to understand…
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint
Stephen Tu, Benjamin Recht
The effectiveness of model-based versus model-free methods is a long-standing question in reinforcement learning (RL). Motivated by recent empirical success of RL on continuous con…