Summary of Offline Multi-task Transfer Rl with Representational Penalization, by Avinandan Bose et al.
Offline Multi-task Transfer RL with Representational Penalization
by Avinandan Bose, Simon Shaolei Du, Maryam Fazel
First submitted to arxiv on: 19 Feb 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 A novel approach to representation transfer is proposed for offline Reinforcement Learning (RL), where agents learn shared representations from episodic data across multiple source tasks. The goal is to leverage these learned representations to solve a target task, without the luxury of online interactions with the environment. Unlike online RL, offline RL relies solely on pre-collected data, which can lead to incomplete coverage and reduced performance. To address this issue, the proposed method combines insights from multi-task learning and representation transfer to develop a shared representation that generalizes well across tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart robot that’s learned how to do lots of different things by watching videos of itself doing those things before. Now, imagine the robot wants to learn how to do something new, like opening a door or picking up a toy. The problem is, it can’t actually open the door or pick up the toy while it’s learning – it just has to use what it learned from the old videos. This is kind of like what happens in offline Reinforcement Learning (RL), where we try to teach robots new skills without letting them actually do anything. The researchers are trying to find a way to make this work better by sharing information between different tasks, so the robot can learn more efficiently and accurately. |
Keywords
* Artificial intelligence * Multi task * Reinforcement learning
