MarioGPT is a finetuned GPT2 model (specifically, distilgpt2), that is trained on Super Mario Bros levels. MarioGPT is able to generate levels, guided by text prompts. This generation is not perfect, but we believe this is a great first step more controllable and diverse level / environment generation.
Appeared in Techcrunch, Kotaku, PCMagazine, Slashgear, Dexerto

Skill Decision Transformer draws inspiration from hindsight relabelling and skill discovery methods to discover a diverse set of primitive behaviors, or skills. We show that Skill DT can not only perform offline state-marginal matching (SMM), but can discovery descriptive behaviors that can be easily sampled. Furthermore, we show that through purely reward-free optimization, Skill DT is still competitive with supervised offline RL approaches on the D4RL benchmark.

In this work, we propose an extension of NCAs to 3D, utilizing 3D convolutions on a voxel grid. We show that despite their simplicity, NCAs are capable of growing complex entities such as castles, apartment blocks, and trees, and even regenerating functional machines. Videos of growth: https://youtu.be/-EzztzKoPeo.
Appeared in Fast Company

NCAs are flexible and robust computational systems, but are inherently uncontrollable during and after their growth process. In this work, we attempt to control these systems using Goal-Guided Neural Cellular Automata (GoalNCA), which leverages goal encodings to control cell behavior dynamically, giving artificial cells the ability to morph and locomote.

Here we propose a new hypernetwork approach to grow artificial neural networks based on neural cellular automata (NCA). Inspired by self-organising systems and information-theoretic approaches to developmental biology, we show that our HyperNCA method can grow neural networks capable of solving common reinforcement learning tasks.

hyper-nn gives users with the ability to create easily customizable Hypernetworks for almost any generic torch.nn.Module from Pytorch and flax.linen.Module from Flax. Our Hypernetwork objects are also torch.nn.Modules and flax.linen.Modules, allowing for easy integration with existing systems. For Pytorch, we make use of the amazing library functorch

A Pytorch based reinforcement learning library focused on providing flexible, modular components that allow for easy customizability and integration into existing workflows. With optim-rl, it becomes trivial to combine algorithms, such as combining PPO's (Proximal Policy Optimization) optimization step with a simple ES (Evolution Strategies) optimization step.

In this project, self driving cars, controlled by neural networks, are evolved using a C# reimplementation of Deep GA (Deep Genetic Algorithm)to drive through a maze.

In this project, popular algorithms are built from scratch using basic libraries such as numpy.
Algorithms implemented: KNN, Linear Discriminant Analysis, Regression, Naive Bayes, Fuzzy Kmeans, PCA, Spectral Clustering.
Optimization algorithms implemented: Stochastic Gradient Descent, Conjugate Gradient Descent, Jacobi Iteration.
In addition, Feedforward neural networks are implemented with working backpropagation.

An interactive multi-agent AI research platform to test state of the art algorithms in customizable battle scenarios where teams of agents compete to survive.

An efficient implementation of Neural Cellular Automatas, written with jax. NCAs are autoregressive models like RNNs, where new states are calculated from previous ones. With jax, we can make these operations a lot more performant with jax.lax.scan and jax.jit