Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal. This guide provides an overview of RL concepts, algorithms, and applications.
Key Concepts
- Agent: The decision-making entity in an RL system.
- Environment: The context in which the agent operates, which provides feedback and rewards based on the agent's actions.
- State: The current situation or configuration of the environment.
- Action: A possible move or decision that the agent can make.
- Reward: A scalar value that indicates how good or bad an action is.
Popular RL Algorithms
- Q-Learning: A value-based RL algorithm that learns a Q-function that estimates the best action for a given state.
- Deep Q-Network (DQN): A combination of Q-learning and deep learning, where a neural network is used to approximate the Q-function.
- Policy Gradient: An algorithm that directly learns a policy, which is a function that maps states to actions.
- SARSA: A policy gradient algorithm that learns a policy by considering the current state, action, reward, and next state.
Applications of RL
- Robotics: Control robots to perform tasks such as navigation and manipulation.
- Game Playing: Develop AI agents that can play games like chess, Go, and poker.
- Autonomous Vehicles: Enable self-driving cars to navigate and make decisions on the road.
- Financial Markets: Create trading agents that can make profitable investments.
Reinforcement Learning Example
For more information on RL, check out our Introduction to Machine Learning.