QLearning

Q-learning is a type of reinforcement learning algorithm that is used to solve problems where an agent interacts with an environment in order to learn the best possible policy (i.e., sequence of actions) to maximize its cumulative reward. It is a type of model-free learning, meaning that it does not require any prior knowledge of the environment or the optimal policy.

At its core, Q-learning involves building a lookup table, called a Q-table, that maps each state-action pair to a value that represents the expected cumulative reward for taking that action in that state. During training, the agent explores the environment by taking actions and updating the Q-table based on the rewards it receives. The agent then uses the Q-table to select the best action to take in each state during the testing phase.

Q-learning has been successfully used to solve a wide range of problems, including game playing, robotics, and autonomous driving. However, it can be computationally expensive and may require a large amount of training data to converge to an optimal policy.