Welcome to the Reinforcement Learning Tutorial! This guide will walk you through the basics of reinforcement learning, a field of machine learning that focuses on how agents learn to make decisions by taking actions in an environment to achieve a goal.
What is Reinforcement Learning?
Reinforcement Learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize some notion of cumulative reward. The agent learns from the consequences of its actions, rather than being explicitly programmed to perform the task.
Key Components of Reinforcement Learning
- Agent: The entity that learns from the environment and makes decisions.
- Environment: The external world that the agent interacts with.
- State: The current situation of the environment.
- Action: The decision made by the agent.
- Reward: The value assigned to the action taken by the agent.
- Policy: The strategy that the agent uses to select actions.
Types of Reinforcement Learning Algorithms
- Value-based Methods: These methods predict the best action for a given state.
- Policy-based Methods: These methods directly learn a policy that maps states to actions.
- Model-based Methods: These methods learn a model of the environment and use it to make decisions.
Common Reinforcement Learning Problems
- Q-Learning: An algorithm that learns the optimal action-value function.
- Deep Q-Networks (DQN): A combination of Q-Learning and deep learning that allows for the training of deep neural networks to approximate the Q-function.
- Policy Gradient Methods: Methods that directly learn a policy by optimizing the expected return.
Getting Started
To get started with reinforcement learning, you can check out our Introduction to Python tutorial. This will help you set up your environment and get familiar with Python, which is commonly used for reinforcement learning.
For further reading, we recommend exploring the Reinforcement Learning: An Introduction book.