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 provide an overview of RL, its basic concepts, and practical examples.

Basic Concepts

  • Agent: The decision-making entity in the environment.
  • Environment: The context in which the agent operates.
  • State: The current situation of the agent.
  • Action: A choice made by the agent.
  • Reward: A signal indicating how good or bad the action was.

Types of RL

  • Tabular RL: The state and action spaces are discrete and finite.
  • Model-based RL: The agent has a model of the environment.
  • Model-free RL: The agent learns directly from the environment without a model.

Key Algorithms

  • Q-Learning: An algorithm that learns the value of an action in a particular state.
  • Sarsa: A variant of Q-Learning that considers the future rewards.
  • Deep Q-Network (DQN): A deep learning algorithm for RL.

Example

Suppose you want to teach a robot to navigate a maze. The robot is the agent, the maze is the environment, and the goal is to reach the exit. The robot will learn to make decisions based on the rewards it receives for reaching the exit or hitting a wall.

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Robot in Maze
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Resources

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