On 24th March, Dr. Akinori Tanaka gave an introduction to the reinforcement learning (RL) in our journal club of the Information Theory Study Group. He started from simple examples of a maze and a chess game to introduce the fundamental variables (i.e., states, actions, and rewards) and their evolution as a Markov decision process.After explaining that the goal of the RL is to maximize the value function, he discussed policy improvement theorem with the application to the epsilon-greedy update. We thank Akinori for the great and clear talk!