Summary of Reinforcement Learning Via Auxiliary Task Distillation, by Abhinav Narayan Harish et al.
Reinforcement Learning via Auxiliary Task Distillation
by Abhinav Narayan Harish, Larry Heck, Josiah P. Hanna, Zsolt Kira, Andrew Szot
First submitted to arxiv on: 24 Jun 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 Reinforcement Learning via Auxiliary Task Distillation (AuxDistill), a novel method is proposed to tackle long-horizon robot control problems. The approach leverages multi-task reinforcement learning, where auxiliary tasks are concurrently performed to learn behaviors relevant to the main task. A weighted distillation loss transfers these learned behaviors to solve the primary objective. The efficacy of AuxDistill is demonstrated by solving a challenging embodied object rearrangement task in the Habitat Object Rearrangement benchmark without relying on demonstrations, curriculum learning, or pre-trained skills. Experimental results show that AuxDistill outperforms state-of-the-art baselines and methods utilizing expert demonstrations, achieving a significant improvement of 2.3 times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AuxDistill is a new way to help robots do things in the long run. It’s like teaching a robot to play many games at once, so it can learn from them all. Then, it uses what it learned from those games to figure out how to do something really hard on its own. This is important because it means we don’t need to give the robot special instructions or show it exactly how to do things. It’s a big step forward in helping robots be more independent and useful. |
Keywords
» Artificial intelligence » Curriculum learning » Distillation » Multi task » Reinforcement learning
