Computer Science > Artificial Intelligence
[Submitted on 18 May 2022 (v1), last revised 24 Jul 2022 (this version, v2)]
Title:Generating Explanations from Deep Reinforcement Learning Using Episodic Memory
View PDFAbstract:Deep Reinforcement Learning (RL) involves the use of Deep Neural Networks (DNNs) to make sequential decisions in order to maximize reward. For many tasks the resulting sequence of actions produced by a Deep RL policy can be long and difficult to understand for humans. A crucial component of human explanations is selectivity, whereby only key decisions and causes are recounted. Imbuing Deep RL agents with such an ability would make their resulting policies easier to understand from a human perspective and generate a concise set of instructions to aid the learning of future agents. To this end we use a Deep RL agent with an episodic memory system to identify and recount key decisions during policy execution. We show that these decisions form a short, human readable explanation that can also be used to speed up the learning of naive Deep RL agents in an algorithm-independent manner.
Submission history
From: Sam Blakeman [view email][v1] Wed, 18 May 2022 13:46:38 UTC (1,256 KB)
[v2] Sun, 24 Jul 2022 17:29:28 UTC (1,256 KB)
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