🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

🇫🇷 Oh my tmux! My self-contained, pretty & versatile tmux configuration made with ❤️

A repository to host extended examples and tutorials

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyp…

JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.

Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.

Computational Dynamics course, MSQE program

A Python and Jsonnet framework for handling espanso configurations

large population models

Scalable Convex Neural Networks

Implimentation of NUQLs method for paper 'Uncertainty Quantification with the Empirical Tangent Kernel'.

Distributed High-Performance Symbolic Regression in Julia

High-Performance Symbolic Regression in Python and Julia

Andy's common tools

A graphical pdf and epub reader that works inside the kitty terminal

macOS development environment setup: Easy-to-understand instructions with automated setup scripts for developer tools like Vim, Sublime Text, Bash, iTerm, Python data analysis, Spark, Hadoop MapRed…

Cost aware hyperparameter tuning algorithm

An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)

Madoko is a fast markdown processor for high quality academic and technical articles

Interactive Web Plotting for Python

A modular framework designed to simulate economies and markets using Reinforcement Learning.

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent

Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)

Codes for the AISTATS 2023 paper, A Statistical Analysis of Polyak-Ruppert Averaged Q-learning.