Search Results for "java 3d game source code"

The Teachingbox uses advanced machine learning techniques to relieve developers from the programming of hand-crafted sophisticated behaviors of autonomous agents (such as robots, game players etc...) In the current status we have implemented a well founded reinforcement learning core in Java with many popular usecases, environments, policies and learners. Obtaining the teachingbox: FOR USERS: If you want to download the latest releases, please visit: http://search.maven.org/#search|ga|1|teachingbox FOR DEVELOPERS: 1) If you use Apache Maven, just add the following dependency to your pom.xml: <dependency> <groupId>org.sf.teachingbox</groupId> <artifactId>teachingbox-core</artifactId> <version>1.2.3</version> </dependency> 2) If you want to check out the most recent source-code: git clone https://git.code.sf.net/p/teachingbox/core teachingbox-core Documentation: https://sourceforge.net/p/teachingbox/documentation/HEAD/tree/trunk/manual/

AlizaGameAPI is a robust, open-source Java-based framework designed to streamline and enhance the development of 2D and 3D games. It offers a comprehensive set of tools, utilities, and libraries, empowering developers to create immersive and dynamic gaming experiences with ease. Key Features: Modular Architecture, Rich Graphics and UI Components, Comprehensive Game Logic and Character Management, Environment and World-Building Tools, Statistical and Mathematical Utilities, Enhanced Debugging and Logging, Data Management and Integration, Integrated LLM for NPC Dialog, Cross-Platform Compatibility Dive into the world of game development with AlizaGameAPI. ...

This package contains code for the game of EasyGo, a modified version of the original game of go. This package provides a simple API so that the users can code and test their own players. Thus one can not only play, but also program their own players.

It's a Cat and Dog game,we develop the Inner AI strategy for the Dog and we also code the strategy of the cat in order to simulate the user. Furthermore we use Artificial Neural Network to build the experiment