SQT -- std $Q$-target
<|reference_start|>SQT -- std $Q$-target: Std $Q$-target is a conservative, actor-critic, ensemble, $Q$-learning-based algorithm, which is based on a single key $Q$-formula: $Q$-networks standard deviation, which is an "uncertainty penalty", and, serves as a minimalistic solution to the problem of overestimation bias. We implement SQT on top of TD3/TD7 code and test it against the state-of-the-art (SOTA) actor-critic algorithms, DDPG, TD3 and TD7 on seven popular MuJoCo and Bullet tasks. Our results demonstrate SQT's $Q$-target formula superiority over TD3's $Q$-target formula as a conservative solution to overestimation bias in RL, while SQT shows a clear performance advantage on a wide margin over DDPG, TD3, and TD7 on all tasks.<|reference_end|>
@article{soffair2024sqt,
    title={SQT -- std $Q$-target},
    author={Nitsan Soffair, Dotan Di-Castro, Orly Avner, Shie Mannor},
    journal={arXiv preprint arXiv:2402.05950},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05950},
    primaryClass={cs.LG cs.AI}
}