That is the basic premise of Policy Gradients for multi-agent models. Each agent is assigned one gradient, which they train on until end. This replaces the up

That is the basic premise of Policy Gradients for multi-agent models. Each agent is assigned one gradient, which they train on until end. This replaces the updating of gradients in distributed systems; and policies seem to work well for Reinforcement Learning.