lukasschaefer Goto Github PK

asnets_fastdownward

ASNet implementation in FastDownward using Keras & Tensorflow

boulderpush

c-seam-carving

C Seam-Carving project for Programming 2 lecture at Saarland University

c_star_project

C project for star maps for the Programming 2 lecture at Saarland University Edit Add topics

connectfour

Implementing Connect Four in Java with JavaFX

cskey

The perfect keyboard layout for all computer scientists.

cv

My personal CV(s)

derl

The official repository of "Decoupling Exploration and Exploitation in Reinforcement Learning" (URL Workshop ICML 2021)

dotfiles

dotfiles for MacOS setup

epymarl

An extension of the PyMARL codebase that includes additional algorithms and environment support

fish

fish setup

hugo_website

Personal webpage with Hugo

jax_playground

Playing around with JAX as acceleration framework

kickstart.nvim

A launch point for your personal nvim configuration

lb-foraging

A multi-agent environment for RL

linux_dotfiles

Dotfiles for Ubuntu with i3, polybar, vim, urxvt, rofi, zsh and more

matrix-games

meltingpot

A suite of test scenarios for multi-agent reinforcement learning.

mips_project

mips-project of programming 2 lecture at Saarland University

msc_curiosity_marl

MSc Informatics dissertation project - University of Edinburgh: Curiosity in Multi-Agent Reinforcement Learning

multiagent-particle-envs

Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

pettingzoo

Gym for multi-agent reinforcement learning

rl2020_building-a-complete-rl-system_demonstration

Demonstration code for the "Building a Complete RL System" lecture of the Reinforcement Learning course at the University of Edinburgh

robotic-warehouse

Multi-Robot Warehouse (RWARE): A multi-agent reinforcement learning environment

sml_interpreter

partial interpreter of functional programming language SML, written in SML

toha

A Hugo theme for personal portfolio

vectorizedmultiagentsimulator

VMAS is a vectorized framework designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.