EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914947597860864 |
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| author | Wang, Shengjie Liu, Shaohuai Ye, Weirui You, Jiacheng Gao, Yang |
| author_facet | Wang, Shengjie Liu, Shaohuai Ye, Weirui You, Jiacheng Gao, Yang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00564 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data Wang, Shengjie Liu, Shaohuai Ye, Weirui You, Jiacheng Gao, Yang Machine Learning Artificial Intelligence Robotics 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. |
| title | EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data |
| topic | Machine Learning Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2403.00564 |