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 will cover the basics of RL, including key concepts, algorithms, and their applications.

Key Concepts

  • Agent: The decision-making entity in an environment.
  • Environment: The system that provides the agent with the state and rewards.
  • State: The current situation of the environment.
  • Action: A decision made by the agent.
  • Reward: The outcome of an action, which guides the agent towards the goal.

Algorithms

  • Q-Learning: A value-based RL algorithm that learns the optimal action-value function.
  • Policy Gradient: An algorithm that learns the optimal policy directly.
  • Deep Q-Network (DQN): A combination of Q-Learning and deep learning to handle high-dimensional state spaces.
  • SARSA: A value-based RL algorithm similar to Q-Learning but uses the current state and action to update the Q-values.

Applications

  • Robotics: Teaching robots to perform tasks such as walking, picking up objects, and navigating environments.
  • Games: Developing AI agents to play games like chess, Go, and video games.
  • Autonomous Vehicles: Training self-driving cars to make decisions on the road.
  • Financial Markets: Building models to predict market trends and make investment decisions.

Reinforcement Learning Diagram

For more information on Reinforcement Learning, check out our Advanced Reinforcement Learning guide.