I am interested in ways to make RL algorithms work "out of the box" by adapting their behavior on-the-fly. This can be through learning hyperparameters, exploration strategies, or even update rules. I am also focusing on ways to train agents which can generalize beyond a single simulated environment, which is crucial if they will ever be useful in real-world settings such as robotics or electronic trading.