Explaining deep adaptive programs via reward decomposition
Adaptation Based Programming (ABP) allows programmers to employ" choice points" at
program locations where they are uncertain about how to best code the program logic.
Reinforcement learning (RL) is then used to automatically learn to make choice-point
decisions to optimize the reward achieved by the program. In this paper, we consider a new
approach to explaining the learned decisions of adaptive programs. The key idea is to
include simple program annotations that define multiple semantically meaningful reward …
program locations where they are uncertain about how to best code the program logic.
Reinforcement learning (RL) is then used to automatically learn to make choice-point
decisions to optimize the reward achieved by the program. In this paper, we consider a new
approach to explaining the learned decisions of adaptive programs. The key idea is to
include simple program annotations that define multiple semantically meaningful reward …
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