Multi-Agent Reinforcement Learning by alaamoheb is a comprehensive open-source library designed to facilitate the development, training, and evaluation of multiple agents acting in shared environments. It includes modular implementations of value-based and policy-based algorithms such as DQN, PPO, MADDPG, and more. The repository supports integration with OpenAI Gym, Unity ML-Agents, and the StarCraft Multi-Agent Challenge, allowing users to experiment in both research and real-world inspired scenarios. With configurable YAML-based experiment setups, logging utilities, and visualization tools, practitioners can monitor learning curves, tune hyperparameters, and compare different algorithms. This framework accelerates experimentation in cooperative, competitive, and mixed multi-agent tasks, streamlining reproducible research and benchmarking.

RunReplicate is a unique tool for running and managing Replicate’s machine learning models, particularly those focused on image generation and processing. Unlike cloud-based solutions, RunReplicate operates entirely in your browser, utilizing IndexedDB for temporary image storage to enhance privacy. With its multi-tab interface, users can run different models simultaneously, facilitating complex workflows and quick comparisons between models or parameters. It also includes advanced safety controls, allowing users to toggle the Safety Checker for more control over the image generation process.