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, which could be a software program or a human.
  • Environment: The context in which the agent operates.
  • State: The current situation or condition of the environment.
  • Action: The decision made by the agent.
  • Reward: The feedback given to the agent based on its action.

Types of RL

  1. Tabular RL: The agent learns from a table of Q-values.
  2. Model-based RL: The agent learns a model of the environment and uses it to plan.
  3. Model-free RL: The agent learns directly from the environment without a model.

Common Algorithms

  • Q-Learning
  • Deep Q-Network (DQN)
  • Policy Gradient Methods
  • SARSA

Getting Started

To dive deeper into Reinforcement Learning, you might want to check out our comprehensive guide on Getting Started with Reinforcement Learning.

Reinforcement Learning Diagram

Resources

Conclusion

Reinforcement Learning is a fascinating field with endless possibilities. By understanding the basics and experimenting with different algorithms, you can unlock the power of RL in your projects.