What is Q-learning?
Q-learning is a model-free reinforcement learning algorithm used to determine the optimal action-selection policy for an agent interacting with a stochastic environment. It is a type of temporal-difference learning where an agent utilizes a Q-table to estimate the quality of actions taken in given states.
Key Components
- States (S): The different situations in which the agent can find itself.
- Actions (A): The choices the agent can make in each state.
- Rewards (R): Feedback received after executing an action in a state, guiding the learning process.
- Q-values (Q): A function that predicts the expected utility of taking a given action in a given state.
Process
The Q-learning algorithm updates the Q-values through a process known as the Bellman equation:
Q(s, a) ← Q(s, a) + α[R + γ max Q(s', a') - Q(s, a)]
Here, α is the learning rate, γ is the discount factor, and max Q(s', a') refers to the maximum expected future rewards from the next state.
Applications
Q-learning is widely used in various domains, including robotics, game playing, and resource management, where agents need to learn how to make optimal decisions through interaction with their environments.
In summary, Q-learning enables agents to learn effective policies to maximize cumulative rewards over time, making it a fundamental technique in reinforcement learning.