EXPLORATION METHOD AND APPARATUS IN MULTI-AGENT REINFORCEMENT LEARNING

EXPLORATION METHOD AND APPARATUS IN MULTI-AGENT REINFORCEMENT LEARNING

An exploration method used by an exploration apparatus in multi-agent reinforcement learning to collect training samples during the training process is provided. The exploration method includes calculating the influence of a selected action of each agent on the actions of other agents in a current s...

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Bibliographische Detailangaben
Hauptverfasser: CHUNG, Euisok, YI, Sungwon, SONG, Hwa Jeon, PARK, Jeon Gue, HAN, Ran, YANG, Jeongmin, YOO, Byunghyun, KIM, Hyun Woo
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Zusammenfassung:An exploration method used by an exploration apparatus in multi-agent reinforcement learning to collect training samples during the training process is provided. The exploration method includes calculating the influence of a selected action of each agent on the actions of other agents in a current state, calculating a linear sum of the value of a utility function representing the action value of each agent and the influence on the actions of the other agent calculated for the selected action of each agent, and obtaining a sample to be used for training an action policy of each agent by probabilistically selecting the action in which the linear sum is the maximum, and the random action.