Cogment

What is Cogment?

Cogment is an open-source AI platform developed by AI Redefined that focuses on enabling human-AI collaboration and leveraging AI for the benefit of humanity. It allows the building, training, and operation of AI agents in simulated and real environments, promoting continuous training of humans and AI together. Key features of Cogment include multi-actor capability for interaction among multiple agents and human users, support for various training methods like reinforcement learning and imitation learning, tech stack agnosticism enabling compatibility with different frameworks and environments, multi-experience learning with multiple agent instances, implementation swapping for seamless transitions between different agent implementations, and hybrid AI integration for different agent types. The platform aims to optimize development and deployment processes, allowing quick iteration cycles between simulated and real environments while providing documentation and community support.

Who created Cogment?

Cogment, an open-source AI platform, was created by AI Redefined. It was launched on September 23, 2020, to facilitate human-AI collaboration for continuous training and operation of AI agents in simulated and real environments. The platform's key features include multi-actor capability, support for various training methods, tech stack agnosticism, and hybrid AI integration. Cogment aims to optimize the development and deployment process with minimal discontinuity between stages.

What is Cogment used for?

• Allow multiple agents and multiple human users to exist, train, and work together within the same environment
• Train agents in various ways using Reinforcement learning, Imitation Learning, Curriculum Learning, etc.
• Develop tech-heterogeneous components working together regardless of the tech stacks used to develop them
• Run multiple instances of the same agent in multiple and distributed trials/experiences
• Swap actors in and out from one implementation of an agent to another, from a human user to another, or from a human user to a trained or untrained agent
• Use multiple reinforcement learning agents with various reward sources
• Mix different kinds of agents: expert systems, doctrines, search, planners, neural networks
• Optimized for minimal discontinuity between development and deployment
• Foster human-AI collaboration and continuous training of humans and AI together
• Integration with different frameworks and environments for seamless operation

Who is Cogment for?

• Researchers
• Developers

How to use Cogment?

To use Cogment, follow these steps:

1. Get Started: Begin by visiting the Cogment website and accessing the open-source platform.
2. Key Features: Familiarize yourself with Cogment's capabilities, such as multi-actor support for collaboration, various training methods like reinforcement learning and imitation learning, and tech stack agnosticism enabling compatibility with different frameworks.
3. Training: Use Cogment to build, train, and operate AI agents in simulated or real environments, promoting continuous training of humans and AI together.
4. Implementation: Utilize implementation swapping to transition seamlessly between different agent implementations, including human users and trained agents.
5. Reward Systems: Leverage multiple sources of rewards for reinforcement learning, including the environment, users, and other agents.
6. Hybrid AI: Integrate different types of agents, such as expert systems and neural networks, enhancing the diversity of AI capabilities.
7. Optimization: Minimize discontinuity between development and deployment stages, allowing for quick iteration cycles between simulated and real environments.
8. Documentation and Support: Access the Cogment website for detailed documentation, core concepts, development guides, CLI, and SDK usage references.
9. Community: Engage with the Cogment community for additional insights and support on utilizing the platform effectively.

By following these steps, users can make the most of Cogment's innovative features and facilitate effective collaboration between humans and AI.