The present disclosure describes systems and methods that include calculating, via a reinforcement learning agent (RLA) controller, a plurality of state-action values based on sensor data representing an observed state, wherein the RLA controller utilizes a deep neural network (DNN) and generating, via a fuzzy controller, a plurality of linear models mapping the plurality of state-action values to the sensor data.
INTERPRETING DATA OF REINFORCEMENT LEARNING AGENT CONTROLLER
2020-10-01
Patent
Electronic Resource
English
IPC: | B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G05D SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES , Systeme zum Steuern oder Regeln nichtelektrischer veränderlicher Größen / G06K Erkennen von Daten , RECOGNITION OF DATA / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
Interpreting data of reinforcement learning agent controller
European Patent Office | 2023
|Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach
ArXiv | 2019
|Improve PID controller through reinforcement learning
IEEE | 2018
|Approximation of Agent Dynamics Using Reinforcement Learning
British Library Conference Proceedings | 2013
|