Q-learning is a fundamental reinforcement learning algorithm used for learning optimal policies in
Markov Decision Processes (MDPs). It is a model-free, off-policy algorithm, meaning it does not require knowledge of the environment's dynamics and can learn from experiences collected under any policy, including a different one from the one it's currently following.
Here's an overview of how Q-learning works :
* Initialization
* Exploration-Exploitation
* Interaction with Environment
* Update Q-values
* Repeat
Through this process,
Q-learning learns to approximate the optimal action-value function (Q-function), which gives the expected cumulative reward of taking an action in a given state and following the optimal policy thereafter. Q-learning has been widely used in various applications, including game playing, robotics, and autonomous systems. It is particularly well-suited for environments where the agent has complete knowledge of the state and action spaces and can easily explore them.
