Many transfer problems require re-using previously optimal decisions for
solving new tasks, which suggests the need for learning algorithms that can
modify the mechanisms for choosing certain actions independently of those for
choosing others. However, there is currently no formalism nor theory for how to
achieve this kind of modular credit assignment. To answer this question, we
define modular credit assignment as a constraint on minimizing the algorithmic
mutual information among feedback signals for different decisions. We introduce
what we call the modularity criterion for testing whether a learning algorithm
satisfies this constraint by performing causal analysis on the algorithm
itself. We generalize the recently proposed societal decision-making framework
as a more granular formalism than the Markov decision process to prove that for
decision sequences that do not contain cycles, certain single-step temporal
difference action-value methods meet this criterion while all policy-gradient
methods do not. Empirical evidence suggests that such action-value methods are
more sample efficient than policy-gradient methods on transfer problems that
require only sparse changes to a sequence of previously optimal decisions.

本研究提出了一种模块化信用分配的形式化理论，通过对算法自身的因果分析，将社会决策制定框架作为马尔可夫决策过程的更精细正式框架，证明了一些单步时序差分行为价值方法满足这一约束条件，而所有政策梯度方法都不满足该约束条件，传输学习的实证研究表明这种行为价值方法在要求对先前最优决策序列进行稀疏更改的情况下，比政策梯度方法具有更高的样本效率。