Cogment - Build, train, and operate AI agents in simulated or real environments shared with humans.

Cogment Build, train, and operate AI agents in simulated or real environments shared with humans.

What is Cogment?

Cogment is an open-source platform designed to address the challenges of continuous training of humans and AI together. It enables the creation, training, and operation of AI agents within simulated or real-world environments, fostering collaboration between humans and AI.

The platform supports a modular approach, reducing computational usage and facilitating validation. Cogment allows for a smooth transition from simulation to real-world deployment, promoting safe and efficient training.

Features

Multi-actor: Allow multiple agents and human users to interact within the same environment.
Multi-method: Train agents using various methods like Reinforcement Learning, Imitation Learning, and Curriculum Learning.
Tech stack agnostic: Develop components using different tech stacks (Pytorch, Keras, Tensorflow, Unity, OpenAI Gym, etc.).
Multi-experience learning: Run multiple instances of agents in distributed trials.
Implementation swapping: Swap actors between different agent implementations, human users, or trained/untrained agents.
Multi-source and retroactive rewards: Multiple RL agents can utilize various reward sources and handle delayed evaluations.
Hybrid AI: Combine different agent types (expert systems, planners, neural networks, etc.).
Optimized for minimal discontinuity between development and deployment: Iterative development with seamless transition between simulated and real environments.

Use Cases

Training AI agents in collaborative or competitive scenarios.
Developing AI systems that require human-in-the-loop learning.
Creating simulations for complex systems involving human and AI interaction.
Building hybrid AI systems that combine different AI approaches.
Orchestrating intelligence ecosystems with the best of human and AI capabilities.
Progressive deployment from simulated training environment to real environment.