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 tutorial will give you a basic understanding of RL and its applications.

Key Concepts

  • Agent: The decision-making entity that interacts with the environment.
  • Environment: The system in which the agent operates.
  • State: The current situation or configuration of the environment.
  • Action: The decision made by the agent.
  • Reward: The feedback signal received by the agent after taking an action.

Common Algorithms

  • Q-Learning
  • Sarsa
  • Deep Q-Network (DQN)

Application Examples

  • Game Playing: Chess, Go, Poker
  • Robotics: Navigation, Manipulation
  • Autonomous Vehicles: Traffic control, parking

Reinforcement Learning Diagram

Further Reading

For more in-depth learning, check out our Advanced Reinforcement Learning Tutorial.

If you have any questions or need further assistance, please feel free to contact us.