# How is centralised training and decentralised execution in multi agent reinforcement learning implemented?

In the paper Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning, it is written

We allow centralised training but require decentralised execution, from which follows that the policies $$\pi^a$$ are known to all agents.

My confusion stems from e.g. the following scenario: In an $$N$$ player game, all $$N$$ players share parameters in a single agent network. In such a scenario, when we move on to decentralised execution, how does this take place if parameters for all $$N$$ players were shared across a single network during training?