ercumentilhan's Stars
openai/baselines
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
aleju/imgaug
Image augmentation for machine learning experiments.
microsoft/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:
LantaoYu/MARL-Papers
Paper list of multi-agent reinforcement learning (MARL)
fo40225/tensorflow-windows-wheel
Tensorflow prebuilt binary for Windows
openai/retro
Retro Games in Gym
seungeunrho/minimalRL
Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
openai/multiagent-particle-envs
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
mgbellemare/Arcade-Learning-Environment
The Arcade Learning Environment (ALE) -- a platform for AI research.
oxwhirl/pymarl
Python Multi-Agent Reinforcement Learning framework
uber-research/deep-neuroevolution
Deep Neuroevolution
Kaixhin/Rainbow
Rainbow: Combining Improvements in Deep Reinforcement Learning
oxwhirl/smac
SMAC: The StarCraft Multi-Agent Challenge
kenjyoung/MinAtar
crowdAI/marLo
Multi Agent Reinforcement Learning using MalmĂ–
EssexUniversityMCTS/gvgai
This is the framework for the General Video Game Competition - http://www.gvgai.net/
GAIGResearch/GVGAI
rubenrtorrado/GVGAI_GYM
her/uosteam
:video_game: uosteam | scripts
sicara/gpumonitor
TF 2.x and PyTorch Lightning Callbacks for GPU monitoring
ashedwards/ILPO
Official implementation of ICML paper Imitating Latent Policies from Observation
StanfordVL/ac-teach
Code for the CoRL 2019 paper AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers
timdebruin/baselines-experience-selection
This repository contains code used for the experiments reported in section 8.5 of Experience Selection in Deep Reinforcement Learning for Control.