Summary of Beyond Human Preferences: Exploring Reinforcement Learning Trajectory Evaluation and Improvement Through Llms, by Zichao Shen et al.
Beyond Human Preferences: Exploring Reinforcement Learning Trajectory Evaluation and Improvement through LLMs
by Zichao Shen, Tianchen Zhu, Qingyun Sun, Shiqi Gao, Jianxin Li
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel framework for preference-based reinforcement learning (PbRL) is proposed, which leverages large language models (LLMs) to generate automatic preferences and optimize conditioned policies. The LLM4PG framework abstracts trajectories, ranks preferences, and reconstructs reward functions to overcome the challenges of designing precise reward functions in intricate game tasks. By harnessing the capabilities of LLMs, this approach accelerates RL convergence and mitigates reliance on specialized human knowledge. Experiments demonstrate the effectiveness of the proposed method in complex environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a way for machines to learn from experience. But it can be hard to design good rewards that tell them what to do. This paper proposes a new approach called preference-based reinforcement learning, which uses people’s preferences as a guide. The problem is that getting this information from humans can be slow and costly. To solve this, the authors developed a system that uses big language models to generate automatic preferences. They tested this on some complex tasks and found it worked really well. |
Keywords
» Artificial intelligence » Reinforcement learning
