  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.
