Multi agent deep q learning takes a dive at exploitation state [D]

I am using double & dueling deep q learning. shortly after reaching epsilon 0.01, the reward starts to go downhill. I am experimenting with different hyper parameters, but would be interested in any similar experiences/ideas.

My guess is that since it is a multi agent scenario, most of the exploration stage, the agents learn the best actions, given kind of random actions from the rest. once epsilon reaches 0.01, the behaviors of the rest of the agents (and thus the …

agent epsilon exploitation ideas machinelearning parameters state