Summary of Hindsight Preference Learning For Offline Preference-based Reinforcement Learning, by Chen-xiao Gao et al.
Hindsight Preference Learning for Offline Preference-based Reinforcement Learning
by Chen-Xiao Gao, Shengjun Fang, Chenjun Xiao, Yang Yu, Zongzhang Zhang
First submitted to arxiv on: 5 Jul 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 The proposed method, Hindsight Preference Learning (HPL), tackles the challenge of offline preference-based reinforcement learning by modeling human preferences using rewards conditioned on future outcomes. This approach captures the holistic perspective of data annotation, where humans assess desirability based on overall outcomes rather than immediate rewards. The reward for each step is calculated by marginalizing over possible future outcomes, approximated by a variational auto-encoder trained on an offline dataset. HPL facilitates credit assignment and can deliver robust rewards across various domains. Empirical studies demonstrate the benefits of HPL in optimizing policies using human preferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline preference-based reinforcement learning optimizes policies using human preferences between pairs of trajectory segments from an offline dataset. The proposed method, Hindsight Preference Learning (HPL), captures the holistic perspective by modeling rewards conditioned on future outcomes. This approach calculates rewards for each step by marginalizing over possible future outcomes, approximated by a variational auto-encoder trained on the offline dataset. |
Keywords
* Artificial intelligence * Encoder * Reinforcement learning
