harshakokel

LLMsPracticalGuide

A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)

arxiv-latex-cleaner

arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv

pomotroid

:tomato: Simple and visually-pleasing Pomodoro timer

Pearl

A Production-ready Reinforcement Learning AI Agent Library brought by the Applied Reinforcement Learning team at Meta.

multi-agent-emergence-environments

Environment generation code for the paper "Emergent Tool Use From Multi-Agent Autocurricula"

HAN

Heterogeneous Graph Neural Network

symbolicai

Compositional Differentiable Programming Library

difflogic

A Library for Differentiable Logic Gate Networks

pyDatalog

a datalog implementation in Python

MicroRTS-Py

A simple and highly efficient RTS-game-inspired environment for reinforcement learning (formerly Gym-MicroRTS)

localturk

Mechanical Turk on your own machine.

Popper

An inductive logic programming system

turkle

Django-based clone of Amazon's Mechanical Turk service running in your local environment.

awesome-neuro-symbolic-ai

A curated list of awesome Neuro-Symbolic AI software frameworks

planner-for-relevant-policies

NCRL

The official Pytorch implementation of the paper Neural Compositional Rule Learning for Knowledge Graph Reasoning

deepstochlog

alphailp

dilp-st

autoplanbench

llm-pddl

Work in progress

Parameter-Seed-Set

Planning tasks succinctly represent labeled transition systems, with each ground action corresponding to a label. This granularity, however, is not necessary for solving planning tasks and can be harmful, especially for model-free methods. In this work, we propose automatic approach to reduce the label sets for planning domains.

RAPid-Learn

parl_minigrid

Planning annotated Reinforcement Learning (PaRL) Agent minigrid enviroment

Powerlifted-PSS

Lifted Planning using Parameter Seed Set

fdr-generator

Classical planning task generator

domain-rubiks-cube

SISA

jelia19-typedmil

trac

Exploring the Capacity of Pretrained Language Models for Reasoning about Actions and Change