The Q-learning algorithm process is an interactive method where the agent learns by exploring the environment and updating the Q-table based on the rewards received. The steps involved in the Q-learning algorithm process include the following: Q-table initialization.

Q-learning is a technique used to compute an optimal policy for a controlled Markov chain based on observations of the system controlled using a non-optimal policy. It has proven to be effective for models with finite state and action space.

We will learn in detail how Q-learning works by using the example of a frozen lake. In this environment, the agent must cross the frozen lake from the start to the goal, without falling into the holes. The best strategy is to reach goals by taking the shortest path.

Explore Dyna-Q, an advanced reinforcement learning algorithm that extends Q-Learning by combining real experiences with simulated planning. Q-Learning is a crucial model-free algorithm in reinforcement learning, focusing on learning the value, or "Q-value," of actions in specific states.