What is Reinforcement Learning? – Overview of How it Works

RL algorithms can be broadly categorized as model-free and model-based. Model-free algorithms do not build an explicit model of the environment, or more rigorously, the MDP. They are closer to trial-and-error algorithms that run experiments with the environment using actions and derive the optimal policy from it directly. Model-free algorithms are either value-based or policy-based. Value-based algorithms consider optimal policy to be a direct result of estimating the value function of every state accurately.