This paper proposes a novel perspective on learning, positing it as the
pursuit of dynamical invariants -- data combinations that remain constant or
exhibit minimal change over time as a system evolves. This concept is
underpinned by both informational and physical principles, rooted in the
inherent properties of these invariants. Firstly, their stability makes them
ideal for memorization and integration into associative networks, forming the
basis of our knowledge structures. Secondly, the predictability of these stable
invariants makes them valuable sources of usable energy, quantifiable as kTln2
per bit of accurately predicted information. This energy can be harnessed to
explore new transformations, rendering learning systems energetically
autonomous and increasingly effective. Such systems are driven to continuously
seek new data invariants as energy sources. The paper further explores several
meta-architectures of autonomous, self-propelled learning agents that utilize
predictable information patterns as a source of usable energy.

这篇论文提出了一种新的学习视角，将其定位为追求动力学不变量，即随着系统演变而保持恒定或呈现最小变化的数据组合。这个概念基于信息和物理原理，根植于这些不变量的固有属性。通过稳定性和可预测性，这些不变量成为理想的记忆和整合到联想网络的基础，同时也是有用能量的宝贵来源。这种能量可以用于探索新的转变，使学习系统在能量上自给自足，并且越来越有效。这篇论文进一步探讨了几个以可预测信息模式为可用能源的自主、自驱的学习代理的元架构。

通过动力学不变量理解学习

Understanding Learning through the Lens of Dynamical Invariants

In open-ended environments, autonomous learning agents must set their own
goals and build their own curriculum through an intrinsically motivated
exploration. They may consider a large diversity of goals, aiming to discover
what is controllable in their environments, and what is not. Because some goals
might prove easy and some impossible, agents must actively select which goal to
practice at any moment, to maximize their overall mastery on the set of
learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a
modular Universal Value Function Approximator with hindsight learning to
achieve a diversity of goals of different kinds within a unique policy and 2)
an automated curriculum learning mechanism that biases the attention of the
agent towards goals maximizing the absolute learning progress. Agents focus
sequentially on goals of increasing complexity, and focus back on goals that
are being forgotten. Experiments conducted in a new modular-goal robotic
environment show the resulting developmental self-organization of a learning
curriculum, and demonstrate properties of robustness to distracting goals,
forgetting and changes in body properties.

该论文提出了一种名为 CURIOUS 的算法，它利用 MODULAR Universal Value Function Approximator 和自动化课程学习机制来实现学习代理的自主目标设定和自我组织学习课程，实现学习目标的快速最优化。