Interpretable Meta-Policies for Neural Decision Making 待解读
新用户看这篇论文该怎么开始
- 先点「赞助解读」，AI 会把论文转成可直接执行的行动清单。
- 看完“可执行改进行动”后，可快速决定是否值得立项。
- 用上面的卡片内容直接发给团队，减少重复阅读。
摘要
Deep Reinforcement Learning remains difficult to interpret and align due to the black-box nature of neural networks. While (neuro-)symbolic approaches improve transparency by incorporating symbolic reasoning, applying them directly to low-level actions results in overly complex, unreadable policies. We introduce NEXUS, a hierarchical RL framework that resolves this tension by combining neural skills with symbolic meta-policies. By shifting transparent reasoning to high-level skills rather than atomic actions, NEXUS drastically reduces symbolic complexity without sacrificing environment performance. Object-centric observations enable high-level reasoning and the design of reward functions and meta-policies by interaction with LLMs. We demonstrate that NEXUS agents learn disentangled skills, are interpretable regarding high-level goals, are better aligned to goals described in the game manual, less prone to reward hacking and more robust to game simplifications than deep RL. Finally, we isolate and evaluate the impact of different meta-policy architectures (neural, symbolic, neuro-symbolic) on environmental return and aligned task performance.
分析报告
暂无报告。点击“分析”开始生成。
个性化解读 与社区共享解读不同
用自己的话告诉 AI 你想要什么样的解读（比如"用大白话讲给非专业人士听"、"重点分析对我们团队 RAG 系统的可迁移性"），生成一份只属于你自己的版本；生成后也可以选择设为"愿意共享"，被更多人看到、点赞。