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Q-learning is a type of reinforcement learning algorithm that is used to train agents to make decisions in an environment. The goal of Q-learning is to find the optimal policy, which is a mapping from states to actions that maximizes the expected long-term reward.

In Q-learning, an agent interacts with an environment by taking actions and receiving rewards. The agent maintains a Q-table, which is a table that stores the expected long-term reward for each action in each state. The agent uses the Q-table to decide which action to take in each state.

The Q-table is updated during the training process using the Q-learning algorithm. The basic idea behind Q-learning is to update the Q-value for a state-action pair using the observed reward and the maximum expected future reward for the next state. The Q-value for a state-action pair is updated using the following equation:

Q(s, a) = Q(s, a) + α [r + γ max(Q(s’, a’)) – Q(s, a)]

where s is the current state, a is the current action, r is the reward received, s’ is the next state, a’ is the next action, α is the learning rate and γ is the discount factor. The learning rate controls how much the agent learns from each experience, and the discount factor controls the importance of future rewards.

In Q-learning the agent starts with random values in the Q-table and it explores the environment by taking different actions. As the agent interacts with the environment, it updates its Q-table based on the rewards it receives, the maximum expected future rewards for the next state, and its current Q-values. The agent continues to update its Q-table until it reaches a satisfactory level of performance.

Q-learning is a popular and powerful algorithm that can be used to train agents to make decisions in a wide range of environments, including games, robotics, and self-driving cars. It is particularly useful in environments where the transition dynamics are unknown, and it is easy to implement and understand.