Summary of Efficient Preference-based Reinforcement Learning Via Aligned Experience Estimation, by Fengshuo Bai et al.
Efficient Preference-based Reinforcement Learning via Aligned Experience Estimation
by Fengshuo Bai, Rui Zhao, Hongming Zhang, Sijia Cui, Ying Wen, Yaodong Yang, Bo Xu, Lei Han
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 SEER method is an efficient preference-based reinforcement learning (PbRL) algorithm that tackles the limitation of requiring substantial human feedback in PbRL training. By integrating label smoothing and policy regularization techniques, SEER reduces overfitting of the reward model and mitigates overestimation bias. Experimental results demonstrate that SEER improves feedback efficiency and outperforms state-of-the-art methods by a large margin across various complex tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SEER is a new approach to training agents without needing lots of human input. Right now, these kinds of systems need a lot of help from humans to figure out what’s good or bad. SEER makes this process more efficient by using special tricks like smoothing out human feedback and being careful when trying new things. This helps the system make better choices and learn faster. |
Keywords
» Artificial intelligence » Overfitting » Regularization » Reinforcement learning
