Decision-Making for Oncoming Traffic Overtaking Scenario using Double DQN

Abstract

Great progress has been made in the field of machine learning in recent years. And learning-based methods have been widely utilized for developing highly autonomous vehicle. To this end, we introduce a reinforcement learning based intelligent autonomous vehicle decision making method for oncoming overtaking scenario. The goal of reinforcement learning is to learn how to take optimal decision in corresponding observations through interactions with the environment using a reward function to estimate whether the decision is good or not. A Double Deep Q-learning (Double DQN) agent was used to learn policies (control strategies) for both longitudinal speed and lane change decision. Prioritized Experience Replay (PER) was used to accelerate convergence of the policies. A two-way 3-car scenario with oncoming traffic was established in SUMO (Simulation of Urban Mobility) to train and test the policies.