What is Reinforcement Learning?
Reinforcement Learning (RL) is a subfield of machine learning, which is itself a branch of artificial intelligence (AI). In RL, an agent learns to make decisions by interacting with its environment. The core idea is that the agent receives feedback in the form of rewards or penalties based on its actions, guiding it to learn optimal strategies over time.
Key Components of Reinforcement Learning
- Agent: The learner or decision-maker.
- Environment: Everything the agent interacts with.
- Actions: The set of moves the agent can make.
- State: A representation of the current position in the environment.
- Reward: A feedback signal, which evaluates the action taken by the agent.
How It Works
The agent observes the current state of the environment, selects an action, receives a reward, and transitions to a new state. The main objective is to learn a policy that maximizes the cumulative reward over time. Through exploration (trying new actions) and exploitation (choosing known rewarding actions), the agent gradually improves its performance.
Applications
Reinforcement Learning has found applications in various domains such as robotics, game playing, finance, and healthcare, demonstrating remarkable success in complex decision-making tasks.