In this lab, you will learn the basics of Chainer and how to use ChainerRL by training an agent to play text-based games with OpenAI Gym on a Jupyter notebook. ChainerRL contains a set of Chainer implementations of deep reinforcement learning (DRL) algorithms. Following the success of DeepMind's Deep Q-Network (DQN) algorithm on Atari games, DRL has been applied to many tasks from playing Go to robot control. ChainerRL runs on top of Chainer, one of the popular Python-based deep learning frameworks, which enables users to intuitively implement many kinds of models, with a lot of flexibility and comparable performance with GPUs. ChainerRL already includes state-of-the-art DRL algorithms from DQN to DDPG to A3C, so that users can use them on their reinforcement learning applications. Prerequisites: Basic knowledge of Python, deep learning and reinforcement learning. This lab utilizes GPU resources in the cloud, you are required to bring your own laptop.