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discrete-optimization

Discrete Optimization is a python library to ease the definition and re-use of discrete optimization problems and solvers.

docker-win-remote-dev-on-macos

Setup a windows container with Openssh and Microsfot Visual Studio buildtools

gnn4rcpsp

Learn to solve RCPSPs using GNNs and model-based solvers

gym-iw

Rollout IW implementation

gym-jsbsim

A reinforcement learning environment for aircraft control using the JSBSim flight dynamics model

hello-github-actions

icaps24-skdecide-tutorial

Tutorial on scikit-decide at ICAPS 2024

or-tools

Google's Operations Research tools

pddl-instances

🌍 PDDL instances covering the International Planning Competitions

ppddl-planner

A planner for PPDDL problems including a MDPSIM client, python bindings and many algorithms like RFF, Improved-LAO*, LDFS among others

pybind11

Seamless operability between C++11 and Python

scikit-decide

AI framework for Reinforcement Learning, Automated Planning and Scheduling

seq_dec_mak

class on sequential decision making in AI

simple-cpp-python

A minimal project showing how to configure and code a project mixing C++ and Python

up-fast-downward

Integration of the planning system Fast Downward with the unified-planning framework.

venus

Venus is a state-of-the-art sound and complete verification toolkit for Relu-based feed-forward neural networks. It can be used to check reachability and local adversarial robustness properties. Venus implements a MILP-based verification method whereby it leverages dependency relations between the ReLU nodes to prune the search tree that needs to be considered during branch-and-bound. It additionally implements methods based on symbolic interval propagation and input domain splitting. Venus is developed at the Verification of Autonomous systems Lab at the Department of Computing, Imperial College London for the purposes of the Assured Autononmy Project.