I recently learned about Q-learning, a reinforcement learning technique that directly estimates the expected value of taking an action in a state.One of the difficulties I see with iterative update techniques is how to set the learning rate hyper parameter. Depending on the environment, a high or low learning rate could be better for more quickly converging on the optimal policy. I'm not sure how it would be formalized, but it seems like setting this parameter is just another optimization problem. I'm wondering if there exist techniques where the agent itself learns it's own learning rate for a given environment? 
Agents that estimate their own learning rate
machine learning