Encoding Distributional Soft Actor-Critic for Autonomous Driving in Multi-lane Scenarios

Overview

E-DSAC combines permutation-invariant vehicle encoding with distributional reinforcement learning to handle varying traffic-agent counts. The study analyzes policy-iteration properties and evaluates multi-lane driving in designed scenarios and real-vehicle experiments.