Introduction to Reinforcement Learning Agents with the Unity Game Engine

Reinforcement Learning is a machine learning method where an agent learns from observations and rewards within an environment. The process is guided by the Bellman Equation, which iteratively calculates the value of a state based on the immediate reward plus the discounted value of future states. This guide provides a step-by-step example of building a navigating robot agent in a 2D environment using the Unity game engine and C#. The implementation uses Q-Learning, where the agent updates the quality (Q-value) of each state-action pair to find an optimal policy for navigating a grid to a goal.