jparkerholder

DvD_ES

Code from the paper "Effective Diversity in Population Based Reinforcement Learning", presented as a spotlight at NeurIPS 2020. This is the Evolution Strategies implementation, but of course the method can be used for gradient based RL algorithms (e.g. TD3).

PB2

Code for the Population-Based Bandits Algorithm, presented at NeurIPS 2020.

ASEBO

Code to run the ASEBO algorithm from the paper: From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization... please get in touch if interested!!

procgen_autorl

ES

Simple ES implementation using ray and numpy

SAC-PyTorch

🧶 Minimal PyTorch Soft Actor Critic (SAC) implementation

autorl.github.io

brain-tokyo-workshop

🧠🗼

Data-Mining_Small-Caps

Using data mining techniques to classify small cap equity returns

hanabi_SAD

Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

jparkerholder.github.io

OffCon3

📴 OffCon^3: SOTA PyTorch SAC and TD3 Implementations (arxiv: 2101.11331)

ray

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 hyperparameter tuning library.

ReadyPolicyOne

🔍 Codebase for the ICML '20 paper "Ready Policy One: World Building Through Active Learning" (arxiv: 2002.02693)

bsuite

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

continual_rl

Continual reinforcement learning baselines: experiment specifications, implementation of existing methods, and common metrics. Easily extensible to new methods.

dataviz-forum

This will serve as the public forum repository to which anyone can post. [Don't put your graded homework here!]

deep-neuroevolution

Deep Neuroevolution

dreamerv2

Mastering Atari with Discrete World Models

pytorch_notebooks

tutorial notebooks

qmss2017

Materials for the 2017 QMSS Python Workshop

rl-generalization-paper

A list of papers regarding generalization in (deep) reinforcement learning

Speeding-Up-CNNs-Using-Random-Feature-Maps

Final project for big data and machine learning.