@inproceedings{
cassel2024warmup,
title={Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes},
author={Asaf Cassel and Aviv Rosenberg},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1c9XHlHTs7}
}
Policy Optimization (PO) methods are among the most popular Reinforcement Learning (RL) algorithms in practice. Recently, Sherman et al. [2023a] proposed a PO-based algorithm with rate-optimal regret guarantees under the linear Markov Decision Process (MDP) model. However, their algorithm relies on a costly pure exploration warm-up phase that is hard to implement in practice. This paper eliminates this undesired warm-up phase, replacing it with a simple and efficient contraction mechanism. Our PO algorithm achieves rate-optimal regret with improved dependence on the other parameters of the problem (horizon and function approximation dimension) in two fundamental settings: adversarial losses with full-information feedback and stochastic losses with bandit feedback.
Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes
Asaf Cassel Aviv Rosenberg