Azul Game AI Agent is a specialized AI solution for the Azul board game competition. Implemented in Python, it models game state, applies Minimax search for deterministic pruning, and leverages Monte Carlo Tree Search to explore stochastic outcomes. The agent uses custom heuristics to evaluate board positions, prioritizing tile placement patterns that yield high points. It supports head-to-head tournament mode, batch simulations, and result logging for performance analysis. Users can tweak algorithm parameters, integrate with custom game environments, and visualize decision trees to understand move selection.

AGM: AI Game Maker is an innovative platform designed for aspiring game developers. It integrates AI technology to streamline the game creation process, offering users tools for designing characters, environments, and game logic. Users can create interactive gameplay experiences with minimal coding knowledge, enabling rapid prototyping and iteration. The AI assists in generating dialogue, artwork, and even music, enhancing overall productivity and creativity in game development.

TexasHoldemAgent provides a modular environment built on Python to train, evaluate, and deploy an AI-powered poker player for heads-up limit Texas Hold’em. It integrates a custom simulation engine with deep reinforcement learning algorithms, including DQN, for iterative policy improvement. Key capabilities include hand state encoding, action space definition (fold, call, raise), reward shaping, and real-time decision evaluation. Users can customize learning parameters, leverage CPU/GPU acceleration, monitor training progress, and load or save trained models. The framework supports batch simulation to test various strategies, generate performance metrics, and visualize win rates, empowering researchers, developers, and poker enthusiasts to experiment with AI-driven gameplay strategies.

This repository provides an end-to-end reinforcement learning framework for StarCraft II gameplay research. The core agent uses Proximal Policy Optimization (PPO) to learn policy networks that interpret observation data from the PySC2 environment and output precise in-game actions. Developers can configure neural network layers, reward shaping, and training schedules to optimize performance. The system supports multiprocessing for efficient sample collection, logging utilities for monitoring training curves, and evaluation scripts for running trained policies against scripted or built-in AI opponents. The codebase is written in Python and leverages TensorFlow for model definition and optimization. Users can extend components such as custom reward functions, state preprocessing, or network architectures to suit specific research objectives.

MultiAgentPacman offers a Python-based game environment where users can implement, visualize, and benchmark multiple AI agents in the Pacman domain. It supports adversarial search algorithms like minimax, expectimax, alpha-beta pruning, as well as custom reinforcement learning or heuristic-based agents. The framework includes a simple GUI, command-line controls, and utilities to log game statistics and compare agent performance under competitive or cooperative scenarios.

BomberManAI is an AI agent designed to play the classic Bomberman game autonomously. Developed in Python, it interfaces with a game environment to perceive map states, available moves, and opponent positions in real time. The core algorithm combines A* pathfinding, breadth-first search for reachability analysis, and a heuristic evaluation function to determine optimal bomb placement and evasion strategies. The agent handles dynamic obstacles, power-ups, and multiple opponents on various map layouts. Its modular architecture enables developers to experiment with custom heuristics, reinforcement learning modules, or alternative decision-making strategies. Ideal for game AI researchers, students, and competitive bot developers, BomberManAI provides a flexible framework for testing and improving autonomous gaming agents.

SoccerAgent is a specialized AI framework designed for developing and training autonomous soccer agents using state-of-the-art multi-agent reinforcement learning (MARL) techniques. It simulates realistic soccer matches in 2D or 3D environments, offering tools to define reward functions, customize player attributes, and implement tactical strategies. Users can integrate popular RL algorithms (such as PPO, DDPG, and MADDPG) via built-in modules, monitor training progress through dashboards, and visualize agent behaviors in real time. The framework supports scenario-based training for offense, defense, and coordination protocols. With an extensible codebase and detailed documentation, SoccerAgent empowers researchers and developers to analyze team dynamics and refine AI-driven gameplay strategies for academic and commercial projects.

GiftSong is an innovative platform that transforms your memories into personalized musical gifts. Whether you want to commemorate a birthday, wedding, or special milestone, you can customize your song by choosing the occasion, musical style, and adding personal touches. Utilizing advanced AI technology, GiftSong generates a unique song tailored just for you, making every gift memorable. Ideal for both intimate moments and larger celebrations, let GiftSong help you craft the perfect melody that resonates with emotions and cherished memories.

MetaHuman Creator offers an intuitive interface to design photorealistic digital humans with unprecedented detail. Users can customize facial features, skin textures, and hair styles using a rich library of assets. The tool streamlines the rigging and animation process for character development, integrating seamlessly with Unreal Engine to deliver high-quality characters ready for animation and gameplay.

DND LLM Game leverages large language models to serve as an AI Dungeon Master, dynamically crafting narrative descriptions, quests, and encounters in response to player prompts. It integrates with OpenAI's GPT API and supports customization of adventure settings, difficulty levels, and NPC personalities. As players describe actions or ask questions in the chat interface, the AI generates vivid scene details, dialogues, and branching story paths on the fly. Developers and game masters can configure the engine via Python scripts, adjust model parameters, and extend the framework to include custom modules, making it a flexible tool for solo RPG sessions or AI-assisted tabletop campaigns.

MultiAgent-Systems-StarCraft2-PySC2-Raw offers a complete toolkit for developing, training, and evaluating multiple AI agents in StarCraft II. It exposes low-level controls for unit movement, targeting, and abilities, while allowing flexible reward design and scenario configuration. Users can easily plug in custom neural network architectures, define team-based coordination strategies, and record metrics. Built on top of PySC2, it supports parallel training, checkpointing, and visualization, making it ideal for advancing research in cooperative and adversarial multi-agent reinforcement learning.

The YGO-Agent framework allows researchers and enthusiasts to develop AI bots that play the Yu-Gi-Oh card game using reinforcement learning. It wraps the YGOPRO game simulator into an OpenAI Gym-compatible environment, defining state representations such as hand, field, and life points, and action representations including summoning, spell/trap activation, and attacking. Rewards are based on win/loss outcomes, damage dealt, and game progress. The agent architecture uses PyTorch to implement DQN, with options for custom network architectures, experience replay, and epsilon-greedy exploration. Logging modules record training curves, win rates, and detailed move logs for analysis. The framework is modular, enabling users to replace or extend components such as the reward function or action space.

PyGame Learning Environment (PLE) is an open-source Python framework designed to simplify the development, testing, and benchmarking of reinforcement learning agents within custom game scenarios. It provides a collection of lightweight Pygame-based games with built-in support for agent observations, discrete and continuous action spaces, reward shaping, and environment rendering. PLE features an easy-to-use API compatible with OpenAI Gym wrappers, enabling seamless integration with popular RL libraries such as Stable Baselines and TensorForce. Researchers and developers can customize game parameters, implement new games, and leverage vectorized environments for accelerated training. With active community contributions and extensive documentation, PLE serves as a versatile platform for academic research, education, and real-world RL application prototyping.

BotPlayers is a versatile open-source framework designed to streamline the development and deployment of AI-driven game-playing agents. It features a flexible environment abstraction layer that supports screen scraping, web APIs, or custom simulation interfaces, allowing bots to interact with various games. The framework includes built-in reinforcement learning algorithms, genetic algorithms, and rule-based heuristics, along with tools for data logging, model checkpointing, and performance visualization. Its modular plugin system enables developers to customize sensors, actions, and AI policies in Python or Java. BotPlayers also offers YAML-based configuration for rapid prototyping and automated pipelines for training and evaluation. With cross-platform support on Windows, Linux, and macOS, this framework accelerates experimentation and production of intelligent game agents.

At its core, Gomoku Battle provides a robust simulation environment where AI agents adhere to a JSON-based protocol to receive board state updates and submit move decisions. Developers can integrate custom strategies by implementing simple Python interfaces, leveraging provided sample bots for reference. The built-in tournament manager automates scheduling of round-robin and elimination matches, while detailed logs capture metrics like win rates, move times, and game histories. Outputs can be exported as CSV or JSON for further statistical analysis. The framework supports parallel execution to accelerate large-scale experiments and can be extended to include custom rule variations or training pipelines, making it ideal for research, education, and competitive AI development.

An AI Football Cup in a Java JADE Environment is an open-source demonstration that leverages the Java Agent DEvelopment Framework (JADE) to simulate a full soccer tournament. It models each player as an autonomous agent with behaviors for movement, ball control, passing, and shooting, coordinating via message passing to implement strategies. The simulator includes referee and coach agents, enforces game rules, and manages tournament brackets. Developers can extend decision-making with custom rules or integrate machine learning modules. This environment illustrates multi-agent communication, teamwork, and dynamic strategy planning within a real-time sports scenario.

FemaleSwitch is a groundbreaking AI agent within the gaming industry that focuses on creating dynamic and engaging female characters. This AI agent helps users craft personalized narratives and unique character arcs, significantly enhancing player interaction and satisfaction. By utilizing advanced algorithms, FemaleSwitch generates immersive dialogues and scenarios designed specifically for female characters, making the gaming experience richer and more diverse.

Pentago Swap AI Agent implements an intelligent opponent for the Pentago Swap game by leveraging a Monte Carlo Tree Search (MCTS) algorithm to explore and evaluate potential game states. At each turn, the agent simulates numerous playouts, scoring resulting board positions to identify moves that maximize win probability. It supports customization of search parameters like simulation count, exploration constant, and playout policy, enabling users to fine-tune performance. The agent includes a command-line interface for head-to-head matches, self-play to generate training data, and a Python API for integration into larger game environments or tournaments. Built with modular code, it facilitates extension with alternative heuristics or neural network evaluators for advanced research and development.

Samsung Ballie is an innovative AI agent that seamlessly integrates with your home environment to offer a range of functionalities including security monitoring, smart device control, and personalized communication. Equipped with advanced sensors and machine learning capabilities, Ballie can navigate your home, monitor activities, and even interact with family members through voice commands. It learns user preferences over time, providing a custom-tailored experience that enhances home life.

AIpacman is an open-source Python project that simulates the Pac-Man game environment for AI experimentation. Users can choose from built-in agents or implement custom ones using search algorithms like DFS, BFS, A*, UCS; adversarial methods such as Minimax with Alpha-Beta pruning and Expectimax; or reinforcement learning techniques like Q-Learning. The framework provides configurable mazes, performance logging, visualization of agent decision-making, and a command-line interface for running matches and comparing scores. It is designed to facilitate educational lessons, research benchmarks, and hobbyist projects in AI and game development.