Q-Learning is a reinforcement learning algorithm used in artificial intelligence. It is used to learn an optimal policy for sequential decision-making in environments with rewards and penalties. Q-Learning uses a value function called the Q-function to estimate the expected value of an action in a given state. The algorithm iteratively adjusts the values of the Q-function while exploring the environment and learning to maximize long-term rewards.

Advantages and Disadvantages


  • Can handle problems with large state and action spaces.
  • Does not require a model of the environment (model-free).


  • Can be inefficient in terms of convergence time.
  • Exploring large state spaces can be challenging without additional techniques.

In summary, Q-Learning is a powerful reinforcement learning technique that enables agents to learn optimal behaviors through interaction with the environment, continuously updating their knowledge of the best actions to take in various situations.

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