What is Q-learning?
Q-learning is a model-free reinforcement learning algorithm used to learn the value of action selections within an environment. It is designed to find the optimal action-selection policy for an agent interacting with a given environment, maximizing the cumulative reward over time.
The core idea of Q-learning is to learn a Q-function, which represents the expected utility of taking a specific action in a given state, and following the optimal policy thereafter. The Q-values (or action-values) are updated iteratively using the Bellman equation, enabling the agent to learn from its experiences. Each time the agent interacts with the environment, it updates its Q-value based on the reward received and the expected future rewards.
Q-learning employs a structure called a Q-table that stores the Q-values for every state-action pair. However, this table can become impractical for complex environments with a large state space. To address this limitation, Deep Q-learning integrates deep neural networks to approximate the Q-function, allowing for better generalization in high-dimensional spaces.
Overall, Q-learning is a significant algorithm in reinforcement learning, enabling agents to learn effective strategies through trial and error, without requiring a model of the environment's dynamics.