@inproceedings{
zhao2024a,
title={A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation},
author={Heyang Zhao and Jiafan He and Quanquan Gu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=s3icZC2NLq}
}
The exploration-exploitation dilemma has been a central challenge in reinforcement learning (RL) with complex model classes. In this paper, we propose a new algorithm, Monotonic  Q-Learning with Upper Confidence Bound (MQL-UCB) for RL with general function approximation. Our key algorithmic design includes (1) a general deterministic policy-switching strategy that achieves low switching cost, (2) a monotonic value function structure with carefully controlled function class complexity, and (3) a variance-weighted regression scheme that exploits historical trajectories with high data efficiency. MQL-UCB achieves minimax optimal regret of $\tilde{O}(d\sqrt{HK})$ when $K$ is sufficiently large and near-optimal policy switching cost of $\tilde{O}(dH)$, with $d$ being the eluder dimension of the function class, $H$ being the planning horizon, and $K$ being the number of episodes. 
   Our work sheds light on designing provably sample-efficient and deployment-efficient Q-learning with nonlinear function approximation.
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
Heyang Zhao Jiafan He Quanquan Gu