📄 中文摘要
研究提出了一种高效的多层次程序，用于压缩马尔可夫决策过程（MDP），以解决具有自然多层结构的顺序决策问题。在该方法中，一个层次的参数化策略家族被视为在更高层次的压缩MDP中的单一动作，同时保留原始MDP的语义和结构。这种方法模仿了处理复杂MDP的自然逻辑。高层次的MDP本身是独立的MDP，具有较低的随机性，可以使用现有算法进行求解。通过这种方式，能够更有效地应对复杂的决策任务。
基于技能的课程的多层次元强化学习
出处: Multi-level meta-reinforcement learning with skill-based curriculum
发布: 2026年3月11日
📄 English Summary
Multi-level meta-reinforcement learning with skill-based curriculum
The study presents an efficient multi-level procedure for compressing Markov Decision Processes (MDPs) to address sequential decision-making problems with a natural multi-level structure. A parametric family of policies at one level is treated as single actions in the compressed MDPs at higher levels, preserving the semantic meanings and structure of the original MDP. This approach mimics the natural logic required to tackle complex MDPs. Higher-level MDPs are independent MDPs with reduced stochasticity, which can be solved using existing algorithms. This methodology enables a more effective handling of complex decision-making tasks.