Tabular Q-learning Based Reinforcement Learning Agent for Autonomous Vehicle Drift Initiation and Stabilization
Szilárd Hunor Tóth, Ádám Bárdos, Zsolt János Viharos
Abstract
This paper aims to report on novel research results about developing a reinforcement learning agent for steady-state vehicle drift motion control. Based on the previous results of this research, the primary goal was to eliminate the problems causing learning instability experienced with the Soft Actor-Critic (SAC) algorithm applying Tabular Q-learning in this work. Trained in a MATLAB/Simulink-based simulation environment, the resulting agent succeeded in this task while being able to smoothly operate the vehicle to achieve and retain the desired target drift state, regardless of the discreet nature of the algorithm used for solving an inherently continuous task.
Topics & Concepts
Reinforcement learningTask (project management)Computer scienceMATLABState (computer science)Stability (learning theory)Motion (physics)Control (management)ReinforcementArtificial intelligenceSimulationControl theory (sociology)Control engineeringMachine learningEngineeringAlgorithmOperating systemSystems engineeringStructural engineeringTraffic control and managementReinforcement Learning in RoboticsAutonomous Vehicle Technology and Safety