Introduction to MindMaker: https://www.youtube.com/watch?v=ERm_pZhAPIA

Blueprints Overview: https://youtu.be/Tuo423NujEk

Discord Group: https://discord.gg/shxFwtmsHa

Add life like realism to video game characters using Deep Reinforcement Learning – a machine learning approach that combines neural networks with a learning model to sculpt agent behavior. Using the DRL Learning Engine and the MindMaker AI Plugin, an NPC can be made to perform any combination of actions involving objects in it's environment simply by changing a reward function.

With these tools, games and simulations can function as OpenAI Gym environments for training autonomous machine learning agents. Possible use cases include robotic simulation, autonomous driving, generative architecture, procedural graphics and much more. For game developers, the use cases for self-optimizing agents include controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), prototyping game design decisions, and automated testing of game builds.

The MindMaker DRL Learning Engine *: A functioning version of the DRL Learning Engine is included with project. Algorithms presently supported in MindMaker DRL for UE 5.1 include Stable Baselines3 : Actor Critic ( A2C ), Deep Deterministic Policy Gradient (DDPG) , Deep Q Network ( DQN ), Proximal Policy Optimization ( PPO ), Soft Actor Critic ( SAC ), Twin Delayed DDPG ( TD3 ). Includes Python source code for the learning engine is included in Content\MindMaker\MindMakerSource

* The DRL Learning Engine is a standalone executable contained within the project files that is launched at the commence of play by the MindMaker AI Plugin