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

• URL: http://arxiv.org/abs/2206.10249v1
• Date: Tue, 21 Jun 2022 10:55:39 GMT
• Title: Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars
• Authors: Mingze Wang, Ziyang Zhang, Grace Hui Yang
• Abstract summary: We propose incorporating natural language voice instructions (NLI) in model-based deep reinforcement learning to train self-driving cars. The results show that NLI can help ease the training process and significantly boost the agents' learning speed.
• Score: 12.716258111815312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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