Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making

2018/02/01 by Jingchu Liu, Pengfei Hou, Liu, Jingchu +7
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Traffic control and management #cs.AI #cs.LG

paper · pdf · doi:10.48550/arxiv.1802.00332

7 pages, 2 figures

arxiv created 2018/02/01 · openalex publication_date 2018/02/01 · arxiv updated 2018/02/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Tactical driving decision making is crucial for autonomous driving systems and has attracted considerable interest in recent years. In this paper, we propose several practical components that can speed up deep reinforcement learning algorithms towards tactical decision making tasks: 1) non-uniform action skipping as a more stable alternative to action-repetition frame skipping, 2) a counter-based penalty for lanes on which ego vehicle has less right-of-road, and 3) heuristic inference-time action masking for apparently undesirable actions. We evaluate the proposed components in a realistic driving simulator and compare them with several baselines. Results show that the proposed scheme provides superior performance in terms of safety, efficiency, and comfort.

Citations

• Playing Atari with Deep Reinforcement Learning
• Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
• Explaining How a Deep Neural Network Trained with End-to-End Learning\n Steers a Car
• Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing
• Combining Neural Networks and Tree Search for Task and Motion Planning in Challenging Environments

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