Summary of Efficientzero V2: Mastering Discrete and Continuous Control with Limited Data, by Shengjie Wang et al.
EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data
by Shengjie Wang, Shaohuai Liu, Weirui Ye, Jiacheng You, Yang Gao
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces EfficientZero V2, a framework for sample-efficient Reinforcement Learning (RL) algorithms that outperforms the current state-of-the-art (SOTA) in diverse tasks. The framework is designed to improve performance across multiple domains, including continuous and discrete actions, visual and low-dimensional inputs. In comparison to DreamerV3, EfficientZero V2 achieves superior outcomes in 50 of 66 evaluated tasks on benchmarks like Atari 100k, Proprio Control, and Vision Control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers learn faster using reinforcement learning. This means teaching computers how to make decisions by giving them rewards or penalties for different actions. The problem is that it takes a long time for computers to learn this way. The researchers created a new tool called EfficientZero V2 that can help solve this problem. It works well in many situations and beats other current methods. |
Keywords
* Artificial intelligence * Reinforcement learning
