Reinforcement learning with Q-learning

Like other machine learning algorithms, a reinforcement learning model needs to be trained before it can be used. The training phase centers on exploring the environment and receiving feedback, given specific actions performed in specific circumstances or states. The life cycle of training a reinforcement learning model is based on the Markov Decision Process, which provides a mathematical framework for modeling decisions. Let's use an autonomous car parking as an example. A simulator needs to model the environment, the actions of the agent, and the rewards received after each action.