Definition
Q-learning is a machine learning algorithm that enables an agent to learn the best actions to take in various scenarios.
In this case, an agent is a software program that decides and acts within a specific environment to achieve the set objectives.
Functioning of Q-learning
Q-learning involves an agent, a Q-table, and an environment. The environment offers various scenarios or states; the agent is given multiple possible actions in each state.
The Q-table assists the agent in recalling the quality (Q) of each action in each state by considering the anticipated rewards.
The agent starts without prior knowledge of the environment and studies it by performing actions and observing the resulting rewards and states.
Q-learning updates the Q-values in the table after every action by applying a formula (the Bellman equation) that incorporates the current action’s reward and anticipated highest future rewards.
The Bellman equation is crucial to Q-learning as it balances immediate rewards and anticipated future rewards. Because the future is uncertain, it prioritizes immediate rewards over distant ones.
The agent continuously updates the Q-table based on the experiences. It employs a technique that combines exploring new actions to uncover their rewards and restating known actions that have been successful in the past. This exploration-exploitation balance enables the agent to learn effectively without falling into poor behavioral patterns.
Q-learning is helpful in scenarios where the environment’s dynamics are unknown or too complicated to model explicitly. It enables the agent to learn by interacting with the environment directly, removing the need for a predefined model.
