Q-Learning
A model-free reinforcement learning algorithm to learn the value of an action in a particular state.
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
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