Advancing Open-source World Models

Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

Learning through interaction with an environment, receiving rewards or penalties to learn optimal behavior policies.

In RL, the learner or decision-maker that takes actions in an environment to maximize cumulative reward.

In RL, the world the agent interacts with, providing states, accepting actions, and returning rewards.

A learned model of environment dynamics that can predict future states, used in model-based RL.

A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

The field of AI that gets computers to extract meaning from images and video: what is in them, where it is, and how it moves.

Creating new images from scratch or from text descriptions using generative models (GANs, diffusion models, VAEs).

A collection of data examples used for training, validating, or testing machine learning models.

The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

Running a trained model on new inputs to get predictions, with its weights frozen: the stage of a model's life that users actually interact with.

The time delay between submitting input and receiving output from a deployed model, critical for real-time applications.