Reward Functions

Reward functions are mathematical functions used in reinforcement learning (RL) to quantify the desirability of an agent's actions or states in an environment. They provide feedback to the agent, guiding it toward optimal behavior by assigning numerical rewards or penalties based on outcomes. This concept is fundamental to RL algorithms, enabling agents to learn policies that maximize cumulative rewards over time.

Also known as: Reward Signal, Reward Model, Utility Function, Objective Function, Cost Function (inverse)

Why learn Reward Functions?

Developers should learn about reward functions when building AI systems that require autonomous decision-making, such as robotics, game AI, or recommendation engines. They are essential for designing effective RL models, as poorly specified reward functions can lead to unintended behaviors or failure to converge. Use cases include training self-driving cars to navigate safely, optimizing resource allocation in logistics, or developing chatbots that improve user engagement.

Learning Resources

• book: Reinforcement Learning: An Introduction (Sutton & Barto)
• tutorial: OpenAI Spinning Up in Deep RL - Reward Functions
• course: DeepMind's RL Course - Reward Design
• docs: Stanford CS234: Reinforcement Learning Lecture Notes
• video: YouTube: Reward Functions in Reinforcement Learning Explained