Federated PPO: Federated Proximal Policy Optimization for Multi-Robot Collision Avoidance
This paper proposes Federated Proximal Policy Optimization (FedPPO), a privacy-preserving federated learning approach that enables efficient and adaptable multi-robot collision avoidance in both homogeneous and heterogeneous systems by aggregating locally trained model parameters to overcome the limitations of independent and shared policy methods.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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