This tutorial will guide you through the basics of Reinforcement Learning (RL), a branch of machine learning that focuses on how agents learn to make decisions in an environment to maximize some notion of cumulative reward.
Overview
Reinforcement Learning is different from supervised learning, where the model is trained on labeled data, and unsupervised learning, where the model learns patterns from unlabeled data. In RL, the agent learns by interacting with the environment and receiving feedback in the form of rewards or penalties.
Key Concepts
- Agent: The decision-making entity that interacts with the environment.
- Environment: The system with which the agent interacts.
- State: A description of the environment's condition.
- Action: A decision made by the agent.
- Reward: The feedback received by the agent for taking an action.
- Policy: The strategy that the agent uses to select actions.
Common RL Algorithms
- Q-Learning: An online learning algorithm that predicts the optimal action to take in a given state.
- Deep Q-Network (DQN): A neural network-based algorithm that combines Q-learning with deep learning to handle complex environments.
- Policy Gradient: An algorithm that learns a policy directly, rather than a value function.
Getting Started
To get started with Reinforcement Learning, we recommend checking out the following resources:
Example Environment
If you're looking for an example environment to practice RL, consider using OpenAI's Gym:
Conclusion
Reinforcement Learning is a fascinating field with many applications, from robotics to gaming to autonomous vehicles. We hope this tutorial has given you a solid foundation to start your journey into the world of RL.