84 citations · 603 across the 59 of their papers we have counts for
6 papers · 1 filter
Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz +5
We introduce a new problem setting for continuous control called the LQR with Rich Observations, or RichLQR. In our setting, the environment is summarized by a low-dimensional cont…
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
Alekh Agarwal, Mikael Henaff, Sham Kakade +1
Direct policy gradient methods for reinforcement learning are a successful approach for a variety of reasons: they are model free, they directly optimize the performance metric of…
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy +1
In order to deal with the curse of dimensionality in reinforcement learning (RL), it is common practice to make parametric assumptions where values or policies are functions of som…
Information Theoretic Regret Bounds for Online Nonlinear Control
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey +2
This work studies the problem of sequential control in an unknown, nonlinear dynamical system, where we model the underlying system dynamics as an unknown function in a known Repro…
Provably Efficient Model-based Policy Adaptation
Yuda Song, Aditi Mavalankar, Wen Sun +1
The high sample complexity of reinforcement learning challenges its use in practice. A promising approach is to quickly adapt pre-trained policies to new environments. Existing met…
Constrained episodic reinforcement learning in concave-convex and knapsack settings
Kianté Brantley, Miroslav Dudik, Thodoris Lykouris +4
We propose an algorithm for tabular episodic reinforcement learning with constraints. We provide a modular analysis with strong theoretical guarantees for settings with concave rew…