独立可控因素
Independently Controllable Factors
摘要：
人们假设，一个好的表示是一个解开变异的潜在解释因素。然而，什么样的培训框架有可能实现这一目标，这仍然是一个悬而未决的问题。而大多数以前的工作集中在静态设置（例如，与图像），我们假设，一些因果因素可以发现，如果学习者被允许与它的环境进行交互。代理人可以尝试不同的行动，并观察其效果。更具体地说，我们假设这些因素中的一些对应于独立可控的环境方面，即，对于环境的每个这样的方面，存在策略和可学习的特征，使得该策略可以在该特征中产生变化，而对解释观察到的数据中的统计变化的其他特征的变化最小。我们提出了一个特定的目标函数来找到这些因素，并通过实验验证，它确实可以解开独立可控的环境方面，没有任何外在的奖励信号。
英文摘要：
It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
