Reinforcement Learning Introduction

Reinforcement Learning (RL) is a branch of machine learning that focuses on how agents should take actions in an environment to maximize some notion of cumulative reward. It is one of the three fundamental approaches in machine learning, alongside supervised learning and unsupervised learning.

Key Concepts

• Agent: The decision-maker that perceives the environment and takes actions.
• Environment: The system that provides the agent with state information and rewards the agent for actions taken.
• State: The description of the agent's situation in the environment.
• Action: The action taken by the agent.
• Reward: The feedback provided to the agent based on the action taken.
• Policy: The strategy used by the agent to select actions.

Types of Reinforcement Learning

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

Challenges

• Exploration vs. Exploitation: The agent must balance between exploring new actions and exploiting known good actions.
• Credit Assignment: Determining which actions contributed to a particular reward.
• Scalability: Learning effective policies for complex environments.

Applications

• Robotics: Teaching robots to navigate and interact with their environment.
• Games: Developing AI agents for games like chess, Go, and poker.
• Autonomous Vehicles: Creating self-driving cars and drones.
• Financial Markets: Optimizing investment strategies.

Reinforcement Learning Diagram

For more information on Reinforcement Learning, check out our Introduction to Machine Learning.