What is Reinforcement Learning?
Reinforcement Learning (RL) is a subset of Machine Learning, which is itself a branch of Artificial Intelligence. Unlike supervised learning, where models learn from labeled data, RL focuses on how agents should take actions in an environment to maximize cumulative rewards.
Key Components:
- Agent: The learner or decision-maker that interacts with the environment.
- Environment: The external system with which the agent interacts, providing feedback based on actions taken.
- Action: Choices made by the agent that affect its state in the environment.
- State: A specific situation in the environment that the agent observes.
- Reward: Feedback signal from the environment based on the agent's action, indicating success or failure.
How It Works:
In RL, an agent learns through trial and error, receiving rewards or penalties based on its actions. The goal is to develop a strategy, known as a policy, that dictates which action to take in a given state to maximize long-term rewards. RL techniques are used in various applications, from game playing to robotics, where continuous learning and adaptation are crucial.
Applications:
Reinforcement Learning is applied in fields such as autonomous driving, resource management, and game AI, demonstrating its capability to solve complex decision-making tasks effectively.