YOUR.GG is a cutting-edge platform designed to help League of Legends players enhance their gameplay through advanced data analysis and AI technology. The platform offers detailed insights and reports on player performance, helping players understand their strengths and areas for improvement. With a focus on both individual and team gameplay, YOUR.GG aims to boost players' performance, engagement, and overall gaming experience.

DuelGenius is an innovative platform designed specifically for League of Legends players. Using advanced AI technology, it delivers personalized coaching to help players refine their tactics, improve their skills, and climb the ranks faster. From instant post-game analysis to long-term performance tracking, DuelGenius provides comprehensive insights tailored to each player's needs. This ensures continuous improvement and a better understanding of in-game strategies, enhancing the overall gaming experience.

Aimlabs is a cutting-edge aim trainer tailored to enhance your performance in competitive gaming. Designed for FPS enthusiasts, the platform offers customized training scenarios, real-time progress tracking, and AI-driven insights to identify and tackle weaknesses. With over 30 million users, Aimlabs provides a comprehensive training experience that includes game-specific tasks, interactive learning plans, and an extensive online library. Whether you're a beginner or a seasoned pro, Aimlabs helps refine your aiming skills, enabling you to achieve your game-specific goals efficiently.

GPT Guesser is a multiplayer game designed to challenge players to guess the original prompts that were used to generate AI-generated texts. Players compete against each other in real-time, making their best guesses based on the AI outputs shown. They race against the clock to accurately identify the prompts, combining knowledge, intuition, and quick thinking in an entertaining and engaging way. The game aims to provide fun interactions while also showcasing the capabilities of AI technology.

MARL-DPP is an open-source framework enabling multi-agent reinforcement learning (MARL) with enforced diversity through Determinantal Point Processes (DPP). Traditional MARL approaches often suffer from policy convergence to similar behaviors; MARL-DPP addresses this by incorporating DPP-based measures to encourage agents to maintain diverse action distributions. The toolkit provides modular code for embedding DPP in training objectives, sampling policies, and managing exploration. It includes ready-to-use integration with standard OpenAI Gym environments and the Multi-Agent Particle Environment (MPE), along with utilities for hyperparameter management, logging, and visualization of diversity metrics. Researchers can evaluate the impact of diversity constraints on cooperative tasks, resource allocation, and competitive games. The extensible design supports custom environments and advanced algorithms, facilitating exploration of novel MARL-DPP variants.

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.

OpenSpiel is a research framework that provides a wide range of environments (from simple matrix games to complex board games such as Chess, Go, and Poker) and implements various reinforcement learning and search algorithms (e.g., value iteration, policy gradient methods, MCTS). Its modular C++ core and Python bindings allow users to plug in custom algorithms, define new games, and compare performance across standard benchmarks. Designed for extensibility, it supports single and multi-agent settings, enabling study of cooperative and competitive scenarios. Researchers leverage OpenSpiel to prototype algorithms quickly, run large-scale experiments, and share reproducible code.

Reinforcement Learning Agents for PettingZoo Games is a Python-based code library delivering off-the-shelf DQN, PPO, and A2C algorithms for multi-agent reinforcement learning on PettingZoo environments. It features standardized training and evaluation scripts, configurable hyperparameters, integrated TensorBoard logging, and support for both competitive and cooperative games. Researchers and developers can clone the repo, adjust environment and algorithm parameters, run training sessions, and visualize metrics to benchmark and iterate quickly on their multi-agent RL experiments.