The tutorial covers a number of important reinforcement learning (RL) algorithms, including policy iteration, Q-Learning, and Neural Fitted Q. In the first part, we will guide you through the general interaction between RL agents and environments, where the agents ought to take actions in order to maximize returns (i.e. cumulative reward). Next, we will implement Policy Iteration, SARSA, and Q-Learning for a simple tabular environment. The core ideas in the latter will be scaled to more complex MDPs through the use of function approximation. Lastly, we will provide a short introduction to deep reinforcement learning and the DQN algorithm.
Don't forget to tag @eemlcommunity in your comment, otherwise they may not be notified.