End-to-end Reinforcement Learning for Autonomous Longitudinal Control Using Advantage Actor Critic with Temporal Context

Abstract

Reinforcement learning has been used widely for autonomous longitudinal control algorithms. However, many existing algorithms suffer from sample inefficiency in reinforcement learning as well as the jerky driving behaviour of the learned systems. In this paper, we propose a reinforcement learning algorithm and a training framework to address these two disadvantages of previous algorithms proposed in this field. The proposed system uses an Advantage Actor Critic (A2C) learning system with recurrent layers to introduce temporal context within the network. This allows the learned system to evaluate continuous control actions based on previous states and actions in addition to current states. Moreover, slow training of the algorithm caused by its sample inefficiency is addressed by utilising another neural network to approximate the vehicle dynamics. Using a neural network as a proxy for the simulator has significant benefit to training as it reduces the requirement for reinforcement learning to query the simulation (which is a major bottleneck) in learning and as both reinforcement learning network and proxy network can be deployed on the same GPU, learning speed is considerably improved. Simulation results from testing in IPG CarMaker show the effectiveness of our recurrent A2C algorithm, compared to an A2C without recurrent layers.