What is Reinforcement Learning?
Reinforcement Learning (RL) is a type of machine learning that focuses on training agents to make sequences of decisions in an environment to maximize a cumulative reward. Unlike supervised learning, where models are trained on labeled data, or unsupervised learning, which involves finding patterns in unlabeled data, reinforcement learning revolves around trial and error learning.
Key Components of Reinforcement Learning
Here are some terminologies used in reinforcement learning:
| Term | Description |
|---|---|
| Agent | The entity or algorithm that makes decisions within the environment. |
| Environment | The external system or surroundings in which the agent operates. |
| State (S) | A representation of the current situation or configuration of the environment. |
| Action (A) | The choices or decisions available to the agent to interact with the environment. |
| Reward (R) | A scalar value that provides feedback to the agent after taking an action in a particular state. It indicates the immediate benefit or cost associated with the action. |
| Policy (π) | A strategy or set of rules that determines the agent's actions based on the current state. |
The Learning Process
Reinforcement learning involves an iterative learning process, typically described as follows:
- The agent starts in an initial state within the environment.
- It selects an action based on its current policy.
- The chosen action modifies the environment, leading to a new state.
- The agent receives a reward for the action taken.
- The agent updates its policy based on the received reward to maximize cumulative rewards over time.
- The process repeats until the agent learns an optimal policy that maximizes its cumulative reward.
Applications of Reinforcement Learning
Reinforcement learning has found numerous applications across various domains, including:
- Game Playing: RL has excelled in games like Chess, Go, and video games, where agents learn to make strategic decisions to win.
- Robotics: RL is used to train robots to perform tasks such as walking, picking and placing objects, and autonomous navigation.
- Autonomous Vehicles: RL helps self-driving cars make real-time decisions to navigate safely and efficiently.
- Finance: RL is applied in portfolio management, algorithmic trading, and risk assessment.
- Healthcare: It is used for optimizing treatment plans and personalized healthcare recommendations.
About Mario Sanchez
Mario is a Staff Engineer specialising in Frontend at Vercel, as well as being a co-founder of Acme and the content management system Sanity. Prior to this, he was a Senior Engineer at Apple.