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. The agent learns from the consequences of its actions, which are represented as rewards or penalties.
Key Concepts
- Agent: The decision-making entity in the environment.
- Environment: The context in which the agent operates.
- State: The current situation or configuration of the environment.
- Action: The decision made by the agent.
- Reward: The consequence of the action taken by the agent.
Types of RL Algorithms
- Value-based RL: Focuses on learning a value function that estimates the expected return from a given state.
- Policy-based RL: Focuses on learning a policy that specifies the optimal action to take in a given state.
- Model-based RL: Focuses on learning a model of the environment to predict the next state and reward based on the current state and action.
Real-world Applications
- Autonomous Vehicles: Learning to navigate roads safely.
- Robotics: Learning to perform tasks in dynamic environments.
- E-commerce: Personalizing recommendations based on user behavior.
- Finance: Algorithmic trading strategies.
Further Reading
To learn more about Reinforcement Learning, we recommend visiting our Reinforcement Learning Course.
Reinforcement Learning