In this blog post, we’ll explore how a reinforcement learning (RL) agent is trained using episodes, how the Q-network updates its policy, and common troubleshooting issues like overfitting and underfitting. This builds on our previous discussion of action execution, reward calculation, and experience replay.

In this blog post, we’ll break down the key concepts covered in Video 7: Action Execution, Reward Calculation & Experience Replay, where we explore how an AI agent learns to navigate a game environment, collects rewards, and improves its decision-making over time.

This video covers how to setup the Neural Network in Godot, and how state representation is going to work for the environment.

This video covers how to setup the Agents Vision of the world, and how its going to move in the world.

This video covers the basics of Deep Q-Networks (DQN).

This video shows how to install Godot and set up an agent in the example game.

This video covers the basics of Q learning, Epsilon-Greedy strategy, and how to include future rewards while calculating the current reward.

Introduction to Reinforcement Learning in Godot: A Beginner’s Guide Reinforcement Learning (RL) might sound like a heavy topic, but when...