-- MindMaker AI Plugin --
- Downloads -
Download MindMaker AI Plugin for UE4 - Win64
Download MindMaker DRL Starter Content & Example Project - Win64
Download MindMaker Remote ML Server - Win64
Download MindMaker Remote ML Example Projects - Win64 UE 4.26
Download MindMaker Remote ML Python Client Example(Stable Baselines)
- Examples & Tutorials -
- CartPole Task: Creating A Custom Reinforcement Learning Environment [Example Video]
- Automated Stock Trading: Build a Bitcoin Bot In Unreal Engine 4 with reinforcement Learning
- Match To Sample: Solving A Memory Puzzle with a Non Player Character
- Further Resources -
Outsmarted: Reinforcement Learning – It’s Promise and Peril
Stable Baselines RL Lib
- Overview -
The MindMaker AI Plugin enables a network connection and automatic launch of a compatible MindMaker Learning Engine, for instance the Deep Reinforcement Learning(DRL) Engine. Using these tools, games and simulations within UE4 can function as environments for training autonomous learning agents. Agents can currently be trained using deep reinforcement learning, a machine learning approach that combines neural networks with a learning model to sculpt agent behavior. With MindMaker, developers and researchers can easily train machine learning agents for 2D, 3D and VR projects.
Possible use cases include robotic simulation, autonomous driving, generative architecture, procedural graphics and much more. MindMaker AI Plugin provides a central platform from which advances in machine learning can reach many of these fields. 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.
A functioning version of the DRL Learning Engine is included in the link to the example project. Algorithms presently supported by the DRL Learning Engine include Stable Baselines RL Lib: 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 ), Trust Region Policy Optimization ( TRPO ), Deep Deterministic Policy Gradient ( DDPG ).
Setup Instructions For Use With DRL Engine Starter Content
1. Download a compatible Learning Engine or use the one included with the example project.
2. Move the learning engine and its accompanying files into the Content directory of your UE Project. The exact location of the learning engine should be "Content\MindMaker\dist\mindmaker\mindmaker.exe" if the location isnt exactly as specified the plugin will not work to automaticaly launch the learning engine at the start of play and you will have to manually launch mindmaker.exe before begining training.
3. Place the MindMaker AI Plugin in the Plugins directory of your UE Project.
4. If you have downloaded the MindMaker DRL Starter Content & Example Project than simply open MindMakerActorBP blueprint or MindMakerAIControlerBP blueprint from the Content\MindMakerStarterContent\Assets\MindMakerStarterContent\MindMakerActorBP directory and begin creating your custom learning agent using the functions supplied. Be sure that the Socket IO address and port your are using is set to http://localhost:3000
For Creating a Custom Learning AI from Scratch
1. Download a compatible Learning Engine or use the one included with the example project
2. Move the learning engine and its accompanying files into the Content directory of your UE Project. The exact location of the learning enginge should be "Content\MindMaker\dist\mindmaker\mindmaker.exe" if the location isnt exactly as specified the plugin will not work to automaticaly launch the learning engine at the start of play and you will have to manually launch mindmaker.exe before begining training
3. Place the MindMaker AI Plugin in the Plugins directory of your UE Project
4. Add a socket IO component to the blueprint you have chosen to work with. A socketIO client is included with Mindmaker AI plugin. Ensure that the Socket IO address and port your are using is set to http://localhost:3000
5. Connect an Event begin play node to a MindMaker Windows node (One of the Plugins Assets) within your blueprint. The MindMaker Windows node can be found under the MindMaker AI blueprints class once the plugin is installed. Currently only MS windows is supported. Once you have MindMaker Windows node connected to an event begin play node, the MindMaker AI Learning Engine will automatically launch at the beginning of play assuming you have placed it in the correct location of your Projects Content Directory.
6. Create the Reward, Action, Obersvation and Launch MindMaker Functions to use with the learning engine. For examples of how to create these, see the /examples directory which includes two maps CartPole and MatchToSample, which can be downloaded with the the starter content.
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