  In this paper, we provide the details of implementing various reinforcement
learning (RL) algorithms for controlling a Cart-Pole system. In particular, we
describe various RL concepts such as Q-learning, Deep Q Networks (DQN), Double
DQN, Dueling networks, (prioritized) experience replay and show their effect on
the learning performance. In the process, the readers will be introduced to
OpenAI/Gym and Keras utilities used for implementing the above concepts. It is
observed that DQN with PER provides best performance among all other
architectures being able to solve the problem within 150 episodes.
