EP 3625731 A1 20200325 - HYBRID REWARD ARCHITECTURE FOR REINFORCEMENT LEARNING
Title (en)
HYBRID REWARD ARCHITECTURE FOR REINFORCEMENT LEARNING
Title (de)
HYBRIDE BELOHNUNGSARCHITEKTUR FÜR VERSTÄRKUNGSLERNEN
Title (fr)
ARCHITECTURE DE RÉCOMPENSE HYBRIDE POUR APPRENTISSAGE PAR RENFORCEMENT
Publication
Application
Priority
- US 201762508340 P 20170518
- US 201762524461 P 20170623
- US 201715634914 A 20170627
- US 2018028743 W 20180421
Abstract (en)
[origin: WO2018212918A1] Aspects provided herein are relevant to machine learning techniques, including decomposing single-agent reinforcement learning problems into simpler problems addressed by multiple agents. Actions proposed by the multiple agents are then aggregated using an aggregator, which selects an action to take with respect to an environment. Aspects provided herein are also relevant to a hybrid reward model.
IPC 8 full level
G06N 3/00 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01)
CPC (source: EP)
G06N 3/006 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 7/01 (2023.01)
Citation (search report)
See references of WO 2018212918A1
Designated contracting state (EPC)
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
Designated extension state (EPC)
BA ME
DOCDB simple family (publication)
DOCDB simple family (application)
US 2018028743 W 20180421; EP 18723249 A 20180421