EXPLORATION METHOD BASED ON REWARD DECOMPOSITION IN MULTI-AGENT REINFORCEMENT LEARNING
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Aug 1, 2024
app pub date -
Nov 22, 2023
filing date -
Jan 26, 2023
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Abstract
Provided is an exploration method based on reward decomposition in multi-agent reinforcement learning. The exploration method includes: generating a positive reward estimation model through neural network training based on training data including states of all agents, actions of all the agents, and a global reward true value; generating, for each of the agents, a first individual utility function based on the global reward true value and generating a second individual utility function using the positive reward estimation model; and determining an action of each of the agents using the first individual utility function and the second individual utility function based on the state of each of the agents.
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