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3d_control_deep_rl

60_days_rl_challenge

Learn Deep Reinforcement Learning in Depth in 60 days

a3c_continuous

A continuous action space version of A3C LSTM in pytorch plus A3G design

algo

101+ coding interview problems with detailed solutions, test cases, and program analysis

alpha-zero-general

A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4

awesome-rl

Reinforcement learning resources curated

awesome-rlhf

A curated list of reinforcement learning with human feedback resources (continually updated)

b-kartal

Config files for my GitHub profile.

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

cs231n.github.io

Public facing notes page

dagstuhlgan

deeprl

Modularized Implementation of Deep RL Algorithms in PyTorch

deeprl-tutorials

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

dice

Generate Diverse Counterfactual Explanations for any machine learning model.

feudal_networks

An implementation of FeUdal Networks for Hierarchical Reinforcement Learning as published : https://arxiv.org/abs/1703.01161

ga3c

Hybrid CPU/GPU implementation of the A3C algorithm for deep reinforcement learning.

gb

A minimal C and Verilog implementation of Nintendo Gameboy - An fast research environment for Reinforcement Learning

gcem

A C++ compile-time math library using generalized constant expressions

grad-cam-pytorch

PyTorch implementation of Grad-CAM

hrl-ep3

Code for our paper: Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies

hsp

Hierarchical Self-Play

leela-zero

Go engine with no human-provided knowledge, modeled after the AlphaGo Zero paper.

loss-landscape

Code for visualizing the loss landscape of neural nets

malmo

Project Malmo is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. We aim to inspire a new generation of research into challenging new problems presented by this unique environment. --- For installation instructions, scroll down to *Getting Started* below, or visit the project page for more information:

mathsdl-spring18

Topics course Mathematics of Deep Learning, NYU, Spring 18

mcts-t

Code for the paper 'Monte Carlo Tree Search for Asymmetric Trees'

multiworld

Multitask Environments for RL

mushroom-rl

Python library for Reinforcement Learning experiments.

muzero-general

MuZero

nips2018-agent

Implementations for the Multi-Agent AI 'Pommermann' challenge @NIPS2018