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. It is a subset of machine learning that focuses on how software agents ought to take actions in an environment to maximize some notion of cumulative reward.
Key Concepts
- Agent: The decision-making entity that learns from the environment.
- Environment: The system with which the agent interacts.
- State: The current situation or context in which the agent operates.
- Action: The decision made by the agent.
- Reward: The feedback given to the agent after taking an action.
Types of Reinforcement Learning
- Tabular RL: The agent learns from a table of known states and actions.
- Model-based RL: The agent builds a model of the environment and uses it to make decisions.
- Model-free RL: The agent learns directly from interactions with the environment without building a model.
Challenges
- Exploration vs. Exploitation: Balancing the need to explore new actions to learn more about the environment with the need to exploit known actions that are known to be effective.
- Credit Assignment: Determining which actions led to a particular outcome.
Applications
- Robotics: Teaching robots to perform tasks like walking or manipulating objects.
- Games: Developing AI for playing games like chess, Go, or video games.
- Autonomous Vehicles: Helping cars learn to drive safely and efficiently.
- Healthcare: Personalizing treatment plans for patients.
Reinforcement Learning Diagram
For more information on Reinforcement Learning, check out our Reinforcement Learning Tutorial.