Although Reinforcement Learning (RL) algorithms acquire sequential behavioral
patterns through interactions with the environment, their effectiveness in
noisy and high-dimensional scenarios typically relies on specific structural
priors. In this paper, we propose a novel and general Structural Information
principles-based framework for effective Decision-Making, namely SIDM,
approached from an information-theoretic perspective. This paper presents a
specific unsupervised partitioning method that forms vertex communities in the
state and action spaces based on their feature similarities. An aggregation
function, which utilizes structural entropy as the vertex weight, is devised
within each community to obtain its embedding, thereby facilitating
hierarchical state and action abstractions. By extracting abstract elements
from historical trajectories, a directed, weighted, homogeneous transition
graph is constructed. The minimization of this graph's high-dimensional entropy
leads to the generation of an optimal encoding tree. An innovative two-layer
skill-based learning mechanism is introduced to compute the common path entropy
of each state transition as its identified probability, thereby obviating the
requirement for expert knowledge. Moreover, SIDM can be flexibly incorporated
into various single-agent and multi-agent RL algorithms, enhancing their
performance. Finally, extensive evaluations on challenging benchmarks
demonstrate that, compared with SOTA baselines, our framework significantly and
consistently improves the policy's quality, stability, and efficiency up to
32.70%, 88.26%, and 64.86%, respectively.

该研究提出了一种基于结构信息原则的有效决策框架，通过信息论的视角，利用一种特定的无监督划分方法生成状态和动作的抽象表示，构建高效的转换图和编码树，引入了基于技能的学习机制，从而改善了单一智能体和多智能体强化学习算法的性能。

基于结构信息原则的有效强化学习

Effective Reinforcement Learning Based on Structural Information  Principles

This paper addresses the problem of summarizing decisions in spoken meetings:
our goal is to produce a concise {\it decision abstract} for each meeting
decision. We explore and compare token-level and dialogue act-level automatic
summarization methods using both unsupervised and supervised learning
frameworks. In the supervised summarization setting, and given true clusterings
of decision-related utterances, we find that token-level summaries that employ
discourse context can approach an upper bound for decision abstracts derived
directly from dialogue acts. In the unsupervised summarization setting,we find
that summaries based on unsupervised partitioning of decision-related
utterances perform comparably to those based on partitions generated using
supervised techniques (0.22 ROUGE-F1 using LDA-based topic models vs. 0.23
using SVMs).

本文研究了口语会议中决策摘要的自动化方法，比较了基于词元和对话行为的两种自动摘要方法，使用了有监督和无监督学习框架，发现基于无监督分区的摘要与使用基于有监督技术生成的分区的摘要相当 (使用 LDA 主题模型的 ROUGE-F1 为 0.22，SVM 的为 0.23)。