Methods relating to the control of autonomous vehicles using a reinforcement learning agent include a plurality of training sessions (110-1, ..., 110-K), in which the agent interacts with an environment, each having a different initial value and yielding a state-action quantile function Zk,τsa=FZksa−1τ dependent on state (s) and action (a). The methods further include a first uncertainty estimation (114) on the basis of a variability measure VarτEkZk,τsa, relating to a variability with respect to quantile τ, of an average EkZk,τsa of the plurality of state-action quantile functions evaluated for a state-action pair; and a second uncertainty estimation (116) on the basis of a variability measure VarkEτZk,τsa, relating to an ensemble variability, for the plurality of state-action quantile functions evaluated for a state-action pair. The state-action pair may either correspond to a tentative decision, which is verified before execution, or to possible decisions by the agent to guide additional training.
MANAGING ALEATORIC AND EPISTEMIC UNCERTAINTY IN REINFORCEMENT LEARNING, WITH APPLICATIONS TO AUTONOMOUS VEHICLE CONTROL
VERWALTUNG VON ALEKTORISCHER UND EPISTEMISCHER UNSICHERHEIT BEIM VERSTÄRKUNGSLERNEN MIT ANWENDUNGEN FÜR AUTONOME FAHRZEUGSTEUERUNG
GESTION DE L'INCERTITUDE ALÉATOIRE ET ÉPISTÉMIQUE DANS L'APPRENTISSAGE PAR RENFORCEMENT, COMPRENANT DES APPLICATIONS POUR LA COMMANDE DE VÉHICULES AUTONOMES
2022-11-09
Patent
Electronic Resource
English
IPC: | G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / 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 |
Quantifying Epistemic and Aleatoric Uncertainty in the Ampair 600 Wind Turbine
British Library Conference Proceedings | 2015
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