MindMaker: Deep Reinforcement Learning (SingleNode)

Video: https://youtu.be/rgQWJ5bkVkk

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, an AI 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 environments for training autonomous 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 include Stable Baselines : Actor Critic ( A2C ), Sample Efficient Actor-Critic with Experience Replay (ACER), Actor Critic using Kronecker-Factored Trust Region ( ACKTR ), Deep Q Network ( DQN ), Proximal Policy Optimization ( PPO ), Soft Actor Critic ( SAC ), Twin Delayed DDPG ( TD3 ). This is a single node version of the algorithms designed for use on a stand alone machine rather than a distributed collection of computers.

* 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