Q-learning is a popular model-free reinforcement learning algorithm used to solve sequential decision-making problems without requiring knowledge of the environment's transition dynamics. In Q-learning, an agent interacts with an environment, receiving observations and rewards. It maintains a table of Q-values, representing the expected future rewards for taking a specific action in a given state. Through an iterative process, the agent updates these Q-values based on the observed rewards, gradually learning an optimal policy. The Q-values are updated using the famous Bellman equation, which considers the maximum expected future reward achievable from each state-action pair. This iterative process continues until the optimal policy is obtained.

Q-learning is a model-free reinforcement learning approach that aims to learn an optimal policy for an agent in an environment. It does not require prior knowledge of the environment's transition probabilities. Instead, the agent learns by interacting with the environment and updating its Q-values, which represent the expected future rewards for each action in a given state. Q-learning uses a greedy or epsilon-greedy exploration strategy to strike a balance between exploration and exploitation. Through repeated iterations, the agent refines its Q-values, eventually converging to an optimal policy. Q-learning has found applications in various fields such as robotics, autonomous driving, and game playing.

Q-learning is a model-free reinforcement learning algorithm that learns a policy in a Markov Decision Process (MDP) without knowing the underlying transition function. It works by iteratively updating an action-value function, known as Q-values, based on the observed rewards. The algorithm explores the environment through learning from experiences, building a table of Q-values that represents the expected future rewards for each state-action pair. By iteratively updating this table, Q-learning can converge to an optimal policy. It is widely used in various domains, from robotic control to game playing.

Q-learning is a model-free reinforcement learning technique where an agent learns to make decisions in an environment by aiming to maximize the expected cumulative rewards. Unlike model-based methods, Q-learning does not require knowledge of the transition probabilities between states. Instead, it utilizes a Q-value function to estimate the quality of each action in a given state. Through trial and error, the agent updates its Q-values based on the rewards it receives, gradually improving its policy over time. This makes Q-learning a powerful tool for solving complex decision-making problems where it is difficult to model the environment accurately.