EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data
<|reference_start|>EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data: Sample efficiency remains a crucial challenge in applying Reinforcement Learning (RL) to real-world tasks. While recent algorithms have made significant strides in improving sample efficiency, none have achieved consistently superior performance across diverse domains. In this paper, we introduce EfficientZero V2, a general framework designed for sample-efficient RL algorithms. We have expanded the performance of EfficientZero to multiple domains, encompassing both continuous and discrete actions, as well as visual and low-dimensional inputs. With a series of improvements we propose, EfficientZero V2 outperforms the current state-of-the-art (SOTA) by a significant margin in diverse tasks under the limited data setting. EfficientZero V2 exhibits a notable advancement over the prevailing general algorithm, DreamerV3, achieving superior outcomes in 50 of 66 evaluated tasks across diverse benchmarks, such as Atari 100k, Proprio Control, and Vision Control.<|reference_end|>
@article{wang2024efficientzero,
    title={EfficientZero V2: Mastering Discrete and Continuous Control with Limited
  Data},
    author={Shengjie Wang, Shaohuai Liu, Weirui Ye, Jiacheng You and Yang Gao},
    journal={arXiv preprint arXiv:2403.00564},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00564},
    primaryClass={cs.LG cs.AI cs.RO}
}