Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes
<|reference_start|>Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes: 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.<|reference_end|>
@article{cassel2024warm-up,
    title={Warm-up Free Policy Optimization: Improved Regret in Linear Markov
  Decision Processes},
    author={Asaf Cassel and Aviv Rosenberg},
    journal={arXiv preprint arXiv:2407.03065},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03065},
    primaryClass={cs.LG stat.ML}
}