How can reinforcement learning be applied to autonomous driving decision making?

RL for autonomous driving: 1) State representation - ego vehicle state, surrounding objects, road information, either low-level (sensor data) or high-level (abstracted scene); 2) Action space - discrete (lane change, accelerate, brake) or continuous (steering, throttle); 3) Reward design - progress toward goal, collision penalty, comfort penalty, rule compliance; 4) Training approaches - simulation-based (sample efficiency critical), sim-to-real transfer, learning from demonstrations (inverse RL, imitation learning bootstrap); 5) Safety during learning - constrained RL, shielding with safety layer, safe exploration. Challenges: Sample efficiency, reward specification capturing human driving behavior, generalization to unseen scenarios, and safety guarantees. Applications include tactical decision making, traffic negotiation, and parking. Hybrid approaches combine RL with model-based planning.