What is Reinforcement Learning?
Reinforcement learning (RL) is a subfield of artificial intelligence that focuses on how agents can learn to make decisions by taking actions in an environment to maximize cumulative rewards. Unlike supervised learning, where a model learns from labeled data, RL agents learn through trial and error, receiving feedback in the form of rewards or punishments based on their actions.
Key Concepts
- Agent: The learner or decision-maker that interacts with the environment.
- Environment: The external system with which the agent interacts.
- Actions: The set of all possible moves the agent can make.
- Rewards: Feedback from the environment relating to the benefits of an action.
- Policy: A strategy that defines the agent's way of choosing actions based on the current state.
How It Works
In reinforcement learning, the agent observes the state of the environment, selects an action, and receives a reward or penalty. The agent updates its policy based on this feedback, aiming to learn the optimal strategy over time. The process is often modeled as a Markov Decision Process (MDP), which provides a mathematical framework for describing the dynamic decision-making process.
Applications
Reinforcement learning has been successfully applied in various domains such as robotics, gaming, finance, and healthcare. Notable examples include AlphaGo and self-driving cars, demonstrating the power of RL in solving complex, sequential decision-making problems.