Description

Q-Learning is a model-free reinforcement learning algorithm used to find an optimal action-selection policy for any given finite Markov decision process. It works by learning an action-value function that ultimately gives the expected utility of taking a given action in a given state and following the optimal policy thereafter. When such an action-value function is learned, the optimal policy can be constructed by simply selecting the action with the highest value in each state.

Examples

• 🎮 Game playing AI
• 🤖 Robot navigation
• 📊 Resource management

Applications