Method and apparatus for performing multi-agent meta reinforcement learning
AssigneeElectronics and Telecommunications Research Institute
InventorsJeong Min YANG, Hyun Woo KIM, Hwa Jeon SONG, Young Hwan SHIN, Byung Hyun YOO, Eui Suk CHUNG
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Abstract
Disclosed is a method and apparatus for performing multi-agent meta reinforcement learning. The method for performing multi-agent meta reinforcement learning may include: selecting an event by extracting trajectory information for a task for pre-learning; defining a local group and a local state including one or more agents based on the selected event; learning a latent vector based on the defined local group and local state; and learning a strategy based on the latent vector and an action of the one or more agents.
Claims20
A method for performing multi-agent meta reinforcement learning, the method comprising:
- selecting an event by extracting trajectory information for a task for pre-learning;
- defining a local group and a local state including one or more agents based on the selected event;
- learning a latent vector based on the defined local group and local state;
- and
- learning a strategy based on the latent vector and an action of the one or more agents.
An apparatus of performing multi-agent meta reinforcement learning, the apparatus comprising:
- at least one processor and at least one memory,
- wherein the processor is configured to:
- select an event by extracting trajectory information for a task for pre-learning;
- define a local group and a local state including one or more agents based on the selected event;
- learn a latent vector based on the defined local group and local state;
- and
- learn a strategy based on the latent vector and an action of the one or more agents.
One or more non-transitory computer readable medium storing one or more instructions,
- wherein the one or more instructions are executed by one or more processors and control an apparatus for performing multi-agent meta reinforcement learning to:
- select an event by extracting trajectory information for a task for pre-learning;
- define a local group and a local state including one or more agents based on the selected event;
- learn a latent vector based on the defined local group and local state;
- and
- learn a strategy based on the latent vector and an action of the one or more agents.
Dependencies are inferred from the claim text.