EfficientZero - Mastering Atari Games with Limited Data

Explore a comprehensive video analysis of the EfficientZero machine learning research paper, which focuses on mastering Atari games with limited data. Delve into the improvements made over the MuZero algorithm, including self-supervised consistency loss, end-to-end prediction of value prefix, and model-based off-policy correction. Gain insights into how EfficientZero achieves super-human performance on Atari games with significantly less data than previous methods. Examine the experimental results and conclusions, understanding the potential impact of this algorithm on future reinforcement learning research and real-world applications.

- Intro & Outline
- MuZero Recap
- EfficientZero improvements
- Self-Supervised consistency loss
- End-to-end prediction of the value prefix
- Model-based off-policy correction
- Experimental Results & Conclusion