Q-learning is a model-free reinforcement learning algorithm that aims to learn the optimal action-value function, Q(s, a), which tells the agent the expected cumulative reward for taking action "a" in state "s" and following the optimal policy thereafter. Q-learning works by iteratively updating the Q-values based on the experiences gathered from interacting with the environment.
In Q-learning, the agent takes an action, receives a reward, and observes the next state. The Q-value is then updated using the following formula: Q(s, a) ← Q(s, a) + α * [R(s, a) + γ * max_a' Q(s', a') - Q(s, a)] Where:
- α is the learning rate
- γ is the discount factor
- R(s, a) is the reward for taking action "a" in state "s"
- max_a' Q(s', a') is the maximum Q-value in the next state "s'" This update rule ensures that the Q-values gradually converge towards the optimal values.