Summary of Learning Future Representation with Synthetic Observations For Sample-efficient Reinforcement Learning, by Xin Liu et al.
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning
by Xin Liu, Yaran Chen, Dongbin Zhao
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In the realm of visual Reinforcement Learning (RL), upstream representation learning significantly impacts downstream policy learning. By employing auxiliary tasks, agents can enhance visual representations in a targeted manner, leading to improved sample efficiency and performance. This paper proposes LFS, a novel self-supervised RL approach that enriches auxiliary training data by synthesizing future observations. LFS eliminates noise through data selection and enables clustering-based temporal association for representation learning. By leveraging synthesized observations, agents can quickly understand and exploit future information without relying on rewards or actions. Experimental results demonstrate state-of-the-art sample efficiency in challenging continuous control tasks and advanced visual pre-training based on action-free video demonstrations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine teaching a machine to learn from videos or games without telling it what to do next. This paper shows how to make this happen by creating fake data that helps the machine understand its surroundings better. The approach, called LFS, uses computer-generated images to train the machine, making it smarter and more efficient in the process. This can be useful for applications like learning from videos or controlling robots without needing rewards or instructions. In experiments, LFS performed well on complex tasks and pre-trained visual models. |
Keywords
» Artificial intelligence » Clustering » Reinforcement learning » Representation learning » Self supervised
