将强化学习建模为双曲空间中的黑箱优化问题，探索无先验环境。
Reinforcement Learning in Hyperbolic Spaces: Models and Experiments
- 在双曲空间中定义动作空间的度量，适配未知环境探索
- 构建统计与动力学模型，支持无先验信息下的多智能体探索
- 从黑箱优化视角统一五类不同场景，适用于几何结构复杂任务
我们研究了五个场景：单个或两个智能体在无任何先验信息的情况下探索未知环境。尽管表面上差异显著，但均可形式化为双曲空间中的强化学习（RL）问题。更精确地说，将动作空间赋予双曲度量是自然的选择。本文引入解决此类问题所需的统计与动力学模型，并基于该框架实现算法。全文以黑箱优化视角审视强化学习，揭示其在几何结构复杂的环境中的统一性与适应性。
原文摘要 · Abstract (English)
We examine five setups where an agent (or two agents) seeks to explore unknown environment without any prior information. Although seemingly very different, all of them can be formalized as Reinforcement Learning (RL) problems in hyperbolic spaces. More precisely, it is natural to endow the action spaces with the hyperbolic metric. We introduce statistical and dynamical models necessary for addressing problems of this kind and implement algorithms based on this framework. Throughout the paper we view RL through the lens of the black-box optimization.
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