Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making

1 Feb 2018Jingchu LiuPengfei HouLisen MuYinan YuChang Huang

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... (read more)

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