What is Q-learning?
Q-learning is a model-free reinforcement learning algorithm that enables an agent to learn the value of optimal actions in a given state. By interacting with an environment, the agent updates its knowledge about the actions through a reward mechanism.
Core Concepts
- Agent: The learner or decision-maker that interacts with the environment.
- Environment: The external context or space where the agent operates.
- State (s): A representation of the current situation of the agent.
- Action (a): The choices available to the agent in a given state.
- Reward (r): Feedback from the environment based on the agent's action.
Q-Values
Q-learning uses Q-values (state-action values) to represent the expected utility of taking a certain action in a particular state. The goal of Q-learning is to learn the optimal Q-values that can guide an agent toward the best actions over time.
Algorithm Overview
The algorithm iteratively updates the Q-values using the formula: Q(s, a) ← Q(s, a) + α[r + γ max Q(s’, a’) - Q(s, a)], where α is the learning rate and γ is the discount factor. Through exploration and exploitation, Q-learning converges to an optimal policy.
Applications
Q-learning has been applied in various domains, such as robotics, game playing, and autonomous systems, showcasing its versatility and effectiveness in learning from interaction.