Hybrid Action-Based Reinforcement Learning for Multiobjective Compatible Autonomous Driving

Summary

Researchers propose MoEC, a multiobjective ensemble-critic reinforcement learning method for autonomous driving. Each critic in the ensemble follows an independent reward function, and the method pairs a hybrid parameterized action space with an uncertainty-based exploration mechanism that supports hybrid actions. In simulator-based and HighD dataset-based multilane highway scenarios, the method learns policies that balance efficiency, action consistency, and safety.