Summary of Top-erl: Transformer-based Off-policy Episodic Reinforcement Learning, by Ge Li et al.
TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning
by Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL) algorithm enables off-policy updates in the Episodic Reinforcement Learning (ERL) framework, addressing the limitations of current ERL methods. In ERL, policies predict entire action trajectories over multiple time steps using trajectory generators like Movement Primitives (MP). TOP-ERL segments long action sequences and estimates state-action values for each segment using a transformer-based critic architecture and n-step return estimation, resulting in efficient and stable training. Empirical results on sophisticated robot learning environments show that TOP-ERL significantly outperforms state-of-the-art RL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new algorithm called Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL). It helps computers learn by trying different actions and seeing what happens. This is important because it makes the learning process faster and more efficient. The researchers used a special kind of computer program to make this happen. They tested their idea on robots and found that it worked really well. |
Keywords
» Artificial intelligence » Reinforcement learning » Transformer
