ScienHub is an innovative online platform that combines a collaborative LaTeX editor with support for clinical research. Key features include AI-enhanced language tools, Git integration, and a modern UI tailored for seamless collaboration among researchers. The platform is built for various user needs, whether for academic papers or clinical trials, offering tools that enhance writing quality and streamline project management processes. ScienHub aims to empower the research community by providing essential resources and a network for sharing knowledge.

The Science App allows users to analyze any claim with both supporting and opposing evidence derived from peer-reviewed scientific research. By using AI to search scientific papers, it links users directly to the sources, providing a balanced analysis of evidence strength and scientific consensus. The platform is designed to aid researchers in streamlining their literature review process while also offering the general public access to evidence-based information in an approachable format.

This repository implements emergent communication in multi-agent reinforcement learning using PyTorch. Users can configure sender and receiver neural networks to play referential games or cooperative navigation, encouraging agents to develop a discrete or continuous communication channel. It offers scripts for training, evaluation, and visualization of learned protocols, along with utilities for environment creation, message encoding, and decoding. Researchers can extend it with custom tasks, modify network architectures, and analyze protocol efficiency, fostering rapid experimentation in emergent agent communication.

MADDPG-Keras delivers a complete framework for multi-agent reinforcement learning research by implementing the MADDPG algorithm in Keras. It supports continuous action spaces, multiple agents, and standard OpenAI Gym environments. Researchers and developers can configure neural network architectures, training hyperparameters, and reward functions, then launch experiments with built-in logging and model checkpointing to accelerate multi-agent policy learning and benchmarking.