The power of cooperation in networks of learning agents

We study the power of cooperation in a network of agents that exchange information with each other to learn faster. In the talk, we show the extent to which cooperation allows to prove performance bounds that are strictly better than the known bounds for non-cooperating agents. Our results are formulated within the online learning setting and hold either when agents learn a single common task or when each agent learns a different task.

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