Using Game-Theory and Decentralization to Scale Multi-Agent Reinforcement Learning Models
SecondMind implements a new multi-agent reinforcement learning method that scales via decentralization.
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When we think about training or learning processes in deep learning solution we typically visualize centralized models. In those architectures a series of central nodes collect and curate datasets which are used to train the models that are deployed across different nodes in a network. Even in distributed scenarios such as multi-agent reinforcement learning(MARL) that can include tens of thousands of nodes running a model the learning models rely on a handful of centralized nodes.
