Reward-Driven Automated Curriculum Learning for Interaction-Aware Self-Driving at Unsignalized Intersections

Understand

In this work, we present a reward-driven automated curriculum reinforcement learning approach for interaction-aware self-driving at unsignalized intersections, taking into account the uncertainties associated with surrounding vehicles (SVs).

• These uncertainties encompass the uncertainty of SVs' driving intention and also the quantity of SVs.
• To deal with this problem, the curriculum set is specifically designed to accommodate a progressively increasing number of SVs.
• By implementing an automated curriculum selection mechanism, the importance weights are rationally allocated across various curricula, thereby facilitating improved sample efficiency and training outcomes.