rssm

A modular PyTorch library designed for learning, training, and deploying world models across various environments.

DreamerV3 World Model RL from Scratch — Educational implementation of model-based reinforcement learning

Curated papers, code, datasets, and benchmarks for medical world models in imaging, EHR trajectories, treatment planning, surgical AI, robotics, and virtual-cell simulation.

Train a neural world model on your own video data, then play it live with WASD. Record → Kaggle → Play

Component-level benchmark for catastrophic forgetting in world models. Two negative results: forgetting does not follow the labelled task-distance axis, and it happens in the encoder, where the usual metrics cannot see it. 375 runs, with code, data and paper.

This project tests that hypothesis using two 1D physical phenomena — heat diffusion and Burgers equation — as elementary World Models, and advection-diffusion as the compound target phenomenon that neither model can represent alone.

A neural network learns Pac-Man, a world model learns to dream the game, and a third agent learns to play entirely inside those dreams. PPO → RSSM world model → imagination training → online dream loop. Custom vectorized NumPy engine, PyTorch, built from scratch.

Sonification and visualization of latent space deviations in industrial RSSM models — reconstructing tacit knowledge of skilled workers through data-driven perceptual transduction.

RSSM-based world model for latent market dynamics, regime detection, and risk management. DreamerV2-style architecture.

World model (Dreamer/RSSM) vs. model-free PPO for cooperative multi-agent control on PettingZoo Multiwalker with shared policy