Honda Motor Co., Ltd.

Anahita Mohseni-Kabir

Kikuo Fujimura

David Francis Isele

A system and method for multi-agent reinforcement learning in a multi-agent environment that include receiving data associated with the multi-agent environment in which an ego agent and a target agent are traveling. The system and method also include learning single agent policies that are respectively associated with the ego agent and the target agent based on the data associated with the multi-agent environment. The system and method additionally include learning a multi-agent policy as an interactive policy that enables the ego agent and the target agent to account for one another while traveling to respective goals within the multi-agent environment based on the single agent policies. The system and method further include implementing the multi-agent policy to control at least one of: the ego agent and the target agent to operate within the multi-agent environment.

System and method for multi-agent reinforcement learning in a multi-agent environment

A system and method for multi-agent reinforcement learning in a multi-agent environment that include receiving data associated with the multi-agent environment in which an ego agent and a target agent are traveling and learning a single agent policy that is based on the data associated with the multi-agent environment and that accounts for operation of at least one of: the ego agent and the target agent individually. The system and method also include learning a multi-agent policy that accounts for operation of the ego agent and the target agent with respect to one another within the multi-agent environment. The system and method further include controlling at least one of: the ego agent and the target agent to operate within the multi-agent environment based on the multi-agent policy.

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Pittsburgh, PA, United States of America

Anahita Mohseni-Kabir