Pinned Repositories
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
ppddl-planner
A planner for PPDDL problems including a MDPSIM client, python bindings and many algorithms like RFF, Improved-LAO*, LDFS among others
simple-cpp-python
A minimal project showing how to configure and code a project mixing C++ and Python
fteicht's Repositories
fteicht/ppddl-planner
A planner for PPDDL problems including a MDPSIM client, python bindings and many algorithms like RFF, Improved-LAO*, LDFS among others
fteicht/gnn4rcpsp
Learn to solve RCPSPs using GNNs and model-based solvers
fteicht/gym-iw
Rollout IW implementation
fteicht/simple-cpp-python
A minimal project showing how to configure and code a project mixing C++ and Python
fteicht/discrete-optimization
Discrete Optimization is a python library to ease the definition and re-use of discrete optimization problems and solvers.
fteicht/docker-win-remote-dev-on-macos
Setup a windows container with Openssh and Microsfot Visual Studio buildtools
fteicht/gym-jsbsim
A reinforcement learning environment for aircraft control using the JSBSim flight dynamics model
fteicht/hello-github-actions
fteicht/icaps24-skdecide-tutorial
Tutorial on scikit-decide at ICAPS 2024
fteicht/or-tools
Google's Operations Research tools
fteicht/pddl-instances
🌍 PDDL instances covering the International Planning Competitions
fteicht/powerlifted
Powerlifted Planner
fteicht/pybind11
Seamless operability between C++11 and Python
fteicht/scikit-decide
AI framework for Reinforcement Learning, Automated Planning and Scheduling
fteicht/seq_dec_mak
class on sequential decision making in AI
fteicht/up-fast-downward
Integration of the planning system Fast Downward with the unified-planning framework.
fteicht/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.