# High variance in performance of q-learning agents trained with same parameters

I am training an agent to play a simple game using double deep q learning. However, the variance in agent performance is very high, even for agents trained with same model parameters. For example, I can train agent A and agent B using the exact parameters and agent A beats B 800 to 200.

I think I understand why this is happening, when training starts the model is initialized with different weights, and this leads the model to find different local max/min.

The above makes it difficult to find optimal parameters.

What are the strategies to reduce this variance? What parameters should I look at tweaking?