Summary of Skills Regularized Task Decomposition For Multi-task Offline Reinforcement Learning, by Minjong Yoo et al.
Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning
by Minjong Yoo, Sangwoo Cho, Honguk Woo
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper presents a skill-based multi-task reinforcement learning (RL) technique that leverages diverse offline datasets to learn shareable knowledge across multiple tasks. The proposed approach employs task decomposition to jointly learn common skills, which are then used as guidance to reformulate each task into achievable subtasks. The authors also introduce a Wasserstein auto-encoder (WAE) to represent both skills and tasks on the same latent space, regularized with a quality-weighted loss term. To improve performance, imaginary trajectories relevant to high-quality skills are added to datasets. Experimental results demonstrate that this approach is robust to mixed configurations of different-quality datasets and outperforms state-of-the-art algorithms for several robotic manipulation and drone navigation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses machine learning to help robots learn new tasks by combining information from multiple tasks. It’s like how a person might learn to ride a bike, walk, and run by practicing all three activities together. The researchers developed a new method that allows robots to break down complex tasks into smaller, more manageable parts. This helps the robots learn faster and make fewer mistakes. The team tested their approach on several tasks, such as picking up objects with a robotic arm or navigating a drone through a course. |
Keywords
» Artificial intelligence » Encoder » Latent space » Machine learning » Multi task » Reinforcement learning
