We propose DistGP: a multi-robot learning method for collaborative learning of a global function using only local experience and computation. We utilise a sparse Gaussian process (GP) model with a factorisation that mirrors the multi-robot structure of the task, and admits distributed training via Gaussian belief propagation (GBP). Our loopy model outperforms Tree-Structured GPs \cite{bui2014tree} and can be trained online and in settings with dynamic connectivity. We show that such distributed, asynchronous training can reach the same performance as a centralised, batch-trained model, albeit with slower convergence. Last, we compare to DiNNO \cite{yu2022dinno}, a distributed neural network (NN) optimiser, and find DistGP achieves superior accuracy, is more robust to sparse communication and is better able to learn continually.

翻译：我们提出DistGP：一种多机器人学习方法，仅利用局部经验和计算实现全局函数的协同学习。我们采用稀疏高斯过程（GP）模型，其因子分解结构映射了任务的多机器人架构，并通过高斯置信传播（GBP）实现分布式训练。我们的环状模型性能优于树结构高斯过程\cite{bui2014tree}，支持动态连接环境下的在线训练。研究表明，这种分布式异步训练能达到与集中式批量训练模型相同的性能，但收敛速度较慢。最后，与分布式神经网络（NN）优化器DiNNO\cite{yu2022dinno}相比，DistGP在精度上表现更优，对稀疏通信具有更强鲁棒性，并展现出更优异的持续学习能力。