Summary of Efficient Multi-task Reinforcement Learning Via Task-specific Action Correction, by Jinyuan Feng et al.
Efficient Multi-Task Reinforcement Learning via Task-Specific Action Correction
by Jinyuan Feng, Min Chen, Zhiqiang Pu, Tenghai Qiu, Jianqiang Yi
First submitted to arxiv on: 9 Apr 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 In this research paper, the authors propose a novel approach called Task-Specific Action Correction (TSAC) to enhance the generalization of robots in multi-task reinforcement learning (MTRL). TSAC decomposes policy learning into two separate policies: a shared policy (SP) and an action correction policy (ACP), which enables agents to learn multiple tasks concurrently. The authors incorporate goal-oriented sparse rewards in ACP, allowing agents to adopt a long-term perspective and generalize across tasks. Experimental evaluations on Meta-World’s MT10 and MT50 benchmarks demonstrate that TSAC outperforms existing state-of-the-art methods, achieving significant improvements in both sample efficiency and effective action execution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about helping robots learn multiple things at the same time. Right now, robots can only do one thing really well because they get too focused on what they’re doing. The authors want to change that by giving them a new way to think. They call it Task-Specific Action Correction (TSAC). TSAC helps robots learn multiple tasks by breaking down their thinking into two parts: one part is for figuring out the main thing they should be doing, and the other part is for adjusting what they’re doing based on specific goals. This makes it easier for robots to generalize and learn new things. |
Keywords
* Artificial intelligence * Generalization * Multi task * Reinforcement learning
