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.

该论文介绍了EfficientZero V2，它是一个用于高效样本学习的强化学习算法的通用框架，在多个领域中扩展了其性能，包括连续和离散动作以及视觉和低维度输入，在有限数据的情况下，EfficientZero V2在各种任务中明显优于现有技术，特别是在Atari 100k、Proprio Control和Vision Control等基准测试中表现出更好的结果。

高效零点V2：以有限数据掌握离散与连续控制