Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars
This addresses the need for a more natural and efficient training interface for self-driving car agents, though it appears incremental as it builds on existing model-based DRL methods.
The paper tackles the problem of sample- and time-inefficiency in deep reinforcement learning for self-driving cars by incorporating natural language voice instructions, which significantly boosts learning speed in the CARLA simulator.
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.