Learn the basic principles of reinforcement learning and develop a learning agent (Deep Learning Network -- CNN network trained with Q Learning) capable of playing classic Atari games. In this context, the neural network improves through in-game experience so as to choose the next best possible action by interpreting the screen's raw pixels along with the current score (action-value Q learning). At the beginning of the lab, students will be given an "intermediate" agent (trained for ~20 hours) and asked to continue the improvement/training process on NVIDIA-provided GPUs. At the end of the lab, students will be able to play against their best network and take home code that they can use to train agents in other Atari games. Prerequisites: Introductory knowledge of Lua and/or Python. This lab utilizes GPU resources in the cloud, you are required to bring your own laptop.