DEf-MARL (Decentralized Execution Framework for Multi-Agent Reinforcement Learning) provides a robust infrastructure to execute and train cooperative agents without centralized controllers. It leverages peer-to-peer communication protocols to share policies and observations among agents, enabling coordination through local interactions. The framework integrates seamlessly with common RL toolkits like PyTorch and TensorFlow, offering customizable environment wrappers, distributed rollout collection, and gradient synchronization modules. Users can define agent-specific observation spaces, reward functions, and communication topologies. DEf-MARL supports dynamic agent addition and removal at runtime, fault-tolerant execution by replicating critical state across nodes, and adaptive communication scheduling to balance exploration and exploitation. It accelerates training by parallelizing environment simulations and reducing central bottlenecks, making it suitable for large-scale MARL research and industrial simulations.

multiagent_envs delivers a modular set of Python-based environments tailored for multi-agent reinforcement learning research and development. It includes scenarios like cooperative navigation, predator-prey, social dilemmas, and competitive arenas. Each environment lets you define the number of agents, observation features, reward functions, and collision dynamics. The framework integrates seamlessly with popular RL libraries such as Stable Baselines and RLlib, allowing vectorized training loops, parallel execution, and easy logging. Users can extend existing scenarios or create new ones by following a simple API, accelerating experimentation with algorithms like MADDPG, QMIX, and PPO in a consistent, reproducible setup.

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.

autogen_multiagent provides a structured way to instantiate, configure, and coordinate multiple AI agents in Python. It offers dynamic agent creation, inter-agent messaging channels, task planning, execution loops, and monitoring utilities. By integrating seamlessly with the OpenAI API, it allows you to assign specialized roles—such as planner, executor, summarizer—to each agent and orchestrate their interactions. This framework is ideal for scenarios requiring modular, scalable AI workflows, such as automated document analysis, customer support orchestration, and multi-step code generation.

multi_agent_celar is designed as a modular AI platform enabling emergent-language communication among multiple intelligent agents in simulated environments. Users can define agent behaviors via policy files, configure environment parameters, and launch coordinated training sessions where agents evolve their own communication protocols to solve cooperative tasks. The framework includes evaluation scripts, visualization tools, and support for scalable experiments, making it ideal for research on multi-agent collaboration, emergent language, and decision-making processes.

MultiAgentPuzzleSolver provides a modular environment where independent AI agents work together to solve puzzles such as sliding tiles, Rubik’s Cube, and logic grids. Agents share state information, negotiate subtask assignments, and apply diverse heuristics to explore the solution space more effectively than single-agent approaches. Developers can plug in new agent behaviors, customize communication protocols, and add novel puzzle definitions. The framework includes tools for real-time visualization of agent interactions, performance metrics collection, and experiment scripting. It supports Python 3.8+, standard libraries, and popular ML toolkits for seamless integration into research projects.

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.

The Multi-LLM Dynamic Agent Router is an open-architecture framework for building AI agent collaborations. It features a dynamic router that directs sub-requests to the optimal language model, and a GraphQL interface to define composite prompts, query results, and merge responses. This enables developers to break complex tasks into micro-prompts, route them to specialized LLMs, and recombine outputs programmatically, yielding higher relevance, efficiency, and maintainability.

仟寻, a recruiting assistant tool, is designed to streamline the job posting process on major job boards such as 51job and Zhaopin. The extension intelligently assists users by automatically completing important fields in the job posting forms and highlights critical information that needs to be filled out, ensuring that job positions are posted accurately and efficiently. To use 仟寻, you must have a MoATS member account, which can be obtained from http://www.moseeker.com.

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会译:对照式翻译 is a powerful AI translation extension that supports bilingual translation, text translation, image translation, PDF translation, and video subtitle translation. Users can easily understand foreign text and enhance their reading comprehension. The tool features intelligent content recognition, allowing for seamless interaction with multilingual websites. With user-friendly options for selecting languages,会译 delivers accurate translations across various formats, making it an essential companion for anyone looking to explore content in different languages.

AI-MultiPrompt is a web extension designed for those who frequently interact with different AI chat models such as ChatGPT, Bard, and others. By allowing users to submit a single prompt to multiple AI chats simultaneously, it streamlines the interaction process. This tool is perfect for researchers, developers, and anyone seeking diverse responses from various AI platforms. With AI-MultiPrompt, you can enhance your experience and explore multiple perspectives on a single topic with ease.

潑頭天眼通, based on Tesseract and Marvinj fuzzy image recognition, automatically fills in verification codes used in student affairs systems. This tool eliminates the fatigue of entering CAPTCHA manually. Once you enter a webpage needing a verification code, the extension acts as your digital assistant, recognizing and filling in the required code instantly. It’s especially useful for students who frequently interact with educational platforms that require CAPTCHA validation.

薯条AI serves as a comprehensive writing assistant tailored for 小红书. It helps users polish their posts by correcting grammatical errors and typos, enriching the text with creative suggestions, and even generating catchy titles to increase engagement. This tool is ideal for influencers and everyday users looking to enhance their content effortlessly and attract more followers.

데이터 도깨비 is a Chrome extension that enhances your experience with public datasets from the South Korean government's official data portal. It caters to users who may not have prior knowledge of data analysis and aims to simplify the process of extracting insights from available public data. By offering a user-friendly interface, it allows individuals to explore, query, and visualize datasets, making it an essential tool for both casual users and professionals looking for data-driven insights.

MultipleChat is a sophisticated chat platform that allows users to interact with multiple advanced AI models simultaneously. With capabilities spanning across various applications, it enables users to leverage the power of AI for decision-making, creative insights, and efficient customer support. The platform is designed for ease of use, offering a seamless interface where one can switch between different AI models based on their needs, leading to cost-effective and smarter communication. Whether for personal use or business applications, MultipleChat provides a unique solution to harness AI technology effectively.

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오후스프링 provides a comprehensive solution for sellers engaged in global trade. This tool leverages artificial intelligence to assist users in efficiently sourcing products and managing listings. Over the past two years, the platform has translated and edited millions of images, enabling sellers to present their products more attractively. The platform aims to reduce the barriers and complexity often associated with international purchasing, streamlining operations for sellers around the world.

Tuned Together offers a scientific approach to evaluating relationship compatibility by analyzing the nuances in communication patterns between partners. Using advanced Language Style algorithms, the platform assesses initial compatibility and predicts the long-term stability of the relationship. This detailed analysis provides users with insights and confidence in their connections, enhancing the chances of building lasting relationships.

达助 is a specialized tool for inviting and managing TikTok influencers. It focuses on providing overseas merchants with comprehensive marketing solutions for their TikTok shops. Through tailored communication strategies, it maximizes product exposure and optimizes promotional effectiveness on the platform. This tool automates invitations, ensuring brands can reach a wide audience of influencers smoothly and efficiently, increasing engagement and conversions.