Summary of Causal Prompting Model-based Offline Reinforcement Learning, by Xuehui Yu et al.
Causal prompting model-based offline reinforcement learning
by Xuehui Yu, Yi Guan, Rujia Shen, Xin Li, Chen Tang, Jingchi Jiang
First submitted to arxiv on: 3 Jun 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 This paper introduces a framework called Causal Prompting Reinforcement Learning (CPRL) that enables model-based offline Reinforcement Learning (RL) to be applied to online systems in highly suboptimal and resource-constrained scenarios. The CPRL framework consists of two phases: the initial phase involves modeling environmental dynamics using Hidden-Parameter Block Causal Prompting Dynamic (Hip-BCPD), which utilizes invariant causal prompts and aligns hidden parameters to generalize to new and diverse online users. In the subsequent phase, a single policy is trained to address multiple tasks through the amalgamation of reusable skills, circumventing the need for training from scratch. The proposed method outperforms contemporary algorithms in experiments conducted across datasets with varying levels of noise, including simulation-based and real-world offline datasets from the Dnurse APP. The contributions of Hip-BCPDs and the skill-reuse strategy to the robustness of performance are separately verified. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CPRL is a new way for computers to learn from data without having to try out lots of different actions. This helps when we don’t have time or it’s not okay to try all those things. The system uses something called “causal prompts” that help it understand what’s happening and make good decisions even when the data is messy or noisy. It also can learn many skills at once, so it doesn’t need to start from scratch each time. This makes CPRL really good at making choices in new situations. |
Keywords
» Artificial intelligence » Prompting » Reinforcement learning
