This course aims to provide an overview of reinforcement learning (RL) algorithms. RL has achieved remarkable success in various applications, including robotic manipulation and autonomous driving. In addition, RL is also used to fine-tune large language models, and the potential of RL is still expanding. We start with basic concepts such as a Markov decision process and the value functions and see popular algorithms such as TD3 and soft actor critic. We will also take a look at recent topics such as offline RL.