Designing Interpretable Approximations to Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2010.14785v2
• Date: Sat, 19 Jun 2021 06:04:29 GMT
• Title: Designing Interpretable Approximations to Deep Reinforcement Learning
• Authors: Nathan Dahlin, Krishna Chaitanya Kalagarla, Nikhil Naik, Rahul Jain, Pierluigi Nuzzo
• Abstract summary: Deep neural networks (DNNs) set the bar for algorithm performance. It may not be feasible to actually use such high-performing DNNs in practice. This work seeks to identify reduced models that not only preserve a desired performance level, but also, for example, succinctly explain the latent knowledge represented by a DNN.
• Score: 14.007731268271902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In an ever expanding set of research and application areas, deep neural networks (DNNs) set the bar for algorithm performance. However, depending upon additional constraints such as processing power and execution time limits, or requirements such as verifiable safety guarantees, it may not be feasible to actually use such high-performing DNNs in practice. Many techniques have been developed in recent years to compress or distill complex DNNs into smaller, faster or more understandable models and controllers. This work seeks to identify reduced models that not only preserve a desired performance level, but also, for example, succinctly explain the latent knowledge represented by a DNN. We illustrate the effectiveness of the proposed approach on the evaluation of decision tree variants and kernel machines in the context of benchmark reinforcement learning tasks.

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