Reproducibility, Reusability, and Robustness in Deep Reinforcement Learning

Mc Gill Professor Joelle Pineau has an insightful presentation on reproducibility in machine learning and especially in deep reinforcement learning. This is a general trend in science that some results sometimes cannot be fully reproduced. In deep reinforcement learning, there is a stochastic component to the results such as the present value of future rewards. She observes that results can vary for reasons that should not matter such as picking up a random seed (to generate random variables) and that the implementation of base cases by different researchers can yield different outcomes. Making the code and the data available for other researchers to reproduce paper results could alleviate some of these problems. She has introduced the Reproducibility Challenge that could be adopted by other scientific conferences.