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Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars

2022-06-21 10:55:39
Mingze Wang, Ziyang Zhang, Grace Hui Yang
arXiv_CV
           
Abstract

This paper presents a novel approach that supports natural language voice instructions to guide deep reinforcement learning (DRL) algorithms when training self-driving cars. DRL methods are popular approaches for autonomous vehicle (AV) agents. However, most existing methods are sample- and time-inefficient and lack a natural communication channel with the human expert. In this paper, how new human drivers learn from human coaches motivates us to study new ways of human-in-the-loop learning and a more natural and approachable training interface for the agents. We propose incorporating natural language voice instructions (NLI) in model-based deep reinforcement learning to train self-driving cars. We evaluate the proposed method together with a few state-of-the-art DRL methods in the CARLA simulator. The results show that NLI can help ease the training process and significantly boost the agents' learning speed.

Abstract (translated)

URL

https://arxiv.org/abs/2206.10249

PDF

https://arxiv.org/pdf/2206.10249.pdf

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