  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.
