_ __ _ __ __ _ __
_____ _____ (_)/ /__ (_)/ /_ ____/ /___ _____ (_)____/ /___
/ ___// ___// // //_// // __/______ / __ // _ \ / ___// // __ // _ \
(__ )/ /__ / // ,< / // /_ /_____// /_/ // __// /__ / // /_/ // __/
/____/ \___//_//_/|_|/_/ \__/ \__,_/ \___/ \___//_/ \__,_/ \___/
<br>
<p align="center">
<a href="https://github.com/airbus/scikit-decide/actions/workflows/ci.yml?query=branch%3Amaster">
<img src="https://img.shields.io/github/actions/workflow/status/airbus/scikit-decide/ci.yml?branch=master&logo=github&label=CI%20status" alt="actions status">
</a>
<a href="https://github.com/airbus/scikit-decide/tags">
<img src="https://img.shields.io/github/tag/airbus/scikit-decide.svg?label=current%20version" alt="version">
</a>
<a href="https://github.com/airbus/scikit-decide/stargazers">
<img src="https://img.shields.io/github/stars/airbus/scikit-decide.svg" alt="stars">
</a>
<a href="https://github.com/airbus/scikit-decide/network">
<img src="https://img.shields.io/github/forks/airbus/scikit-decide.svg" alt="forks">
</a>
</p>
<br>
# Scikit-decide for Python
Scikit-decide is an AI framework for Reinforcement Learning, Automated Planning and Scheduling.
This framework was initiated at [Airbus](https://www.airbus.com) AI Research and notably received contributions through the [ANITI](https://aniti.univ-toulouse.fr/en/) and [TUPLES](https://tuples.ai/) projects, and also from [ANU](https://www.anu.edu.au/).
## Main features
<!--features-list-start-->
- **Problem solving:** describe your decision-making problem once and auto-match compatible solvers.\
_For instance planning/scheduling problems can be solved by RL solvers using GNNs._
- **Growing catalog:** enjoy a growing list of domains & solvers catalog, supported by the community.
- **Open & Extensible:** scikit-decide is open source and is able to wrap existing state-of-the-art domains/solvers.
- **Domains available:**
- [Gym(nasium)](https://gymnasium.farama.org/) environments for reinforcement learning (RL)
- [PDDL](https://planning.wiki/) (Planning Domain Definition Language) via [unified-planning](https://github.com/aiplan4eu/unified-planning) and [plado](https://github.com/massle/plado) libraries
- encoding in gym(nasium) spaces compatible with RL
- graph representations for RL (inspired by [Lifted Learning Graph](https://doi.org/10.1609/aaai.v38i18.29986)) :new:
- [RDDL](https://users.cecs.anu.edu.au/~ssanner/IPPC_2011/RDDL.pdf) (Relational Dynamic Influence Diagram Language) using [pyrddl-gym](https://github.com/pyrddlgym-project) library.
- Flight planning, based on [openap](https://openap.dev/) or in-house Poll-Schumann for performance model
- Scheduling, based on rcpsp problem from [discrete-optimization](https://airbus.github.io/discrete-optimization) library
- Toy domains like: maze, mastermind, rock-paper-scissors
- **Solvers available:**
- RL solvers from ray.rllib and stable-baselines3
- existing algos with action masking
- adaptation of RL algos for graph observation, based on GNNs from [pytorch-geometric](https://pytorch-geometric.readthedocs.io/) :new:
- autoregressive models with action masking component by component for parametric actions :new:
- Planning solvers from [unified-planning](https://github.com/aiplan4eu/unified-planning) library
- RDDL solvers jax and gurobi-based based on pyRDDLGym-jax and pyRDDLGym-gurobi from [pyrddl-gym project](https://github.com/pyrddlgym-project)
- Search solvers coded in scikit-decide library:
- A*
- AO*
- Improved-LAO*
- Learning Real-Time A*
- Best First Width Search
- Labeled RTDP
- Multi-Agent RTDP
- Iterated Width search (IW)
- Rollout IW (RIW)
- Partially-Observable Monte Carlo Planning (POMCP)
- Monte Carlo Tree Search Methods (MCTS)
- Multi-Agent Heuristic meta-solver (MAHD)
- Evolution strategy: Cartesian Genetic Programming (CGP)
- Scheduling solvers from [discrete-optimization](https://airbus.github.io/discrete-optimization),
- itself wrapping [ortools](https://developers.google.com/optimization), [gurobi](https://www.gurobi.com/),
[toulbar](https://toulbar2.github.io/toulbar2/#), [minizinc](https://www.minizinc.org/),
[deap](https://deap.readthedocs.io/) (genetic algorithm), [didppy](https://didppy.readthedocs.io/) (dynamic programming),
- and coding local search (hill climber, simulated annealing), Large Neighborhood Search (LNS), and
genetic programming based hyper-heuristic (GPHH)
- **Tuning solvers hyperparameters**
- hyperparameters definition
- automated study with optuna
<!--features-list-end-->
## Installation
Quick version:
```shell
pip install scikit-decide[all]
```
For more details, see the [online documentation](https://airbus.github.io/scikit-decide/install).
## Documentation
The latest documentation is available [online](https://airbus.github.io/scikit-decide).
## Examples
Some educational notebooks are available in `notebooks/` folder.
Links to launch them online with [binder](https://mybinder.org/) are provided in the
[Notebooks section](https://airbus.github.io/scikit-decide/notebooks) of the online documentation.
More examples can be found as Python scripts in the `examples/` folder, showing how to import or define a domain,
and how to run or solve it. Most of the examples rely on scikit-decide Hub, an extensible catalog of domains/solvers.
## Contributing
See more about how to contribute in the [online documentation](https://airbus.github.io/scikit-decide/contribute).
Raw data
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"description": "\n _ __ _ __ __ _ __\n _____ _____ (_)/ /__ (_)/ /_ ____/ /___ _____ (_)____/ /___\n / ___// ___// // //_// // __/______ / __ // _ \\ / ___// // __ // _ \\\n (__ )/ /__ / // ,< / // /_ /_____// /_/ // __// /__ / // /_/ // __/\n /____/ \\___//_//_/|_|/_/ \\__/ \\__,_/ \\___/ \\___//_/ \\__,_/ \\___/\n\n<br>\n<p align=\"center\">\n <a href=\"https://github.com/airbus/scikit-decide/actions/workflows/ci.yml?query=branch%3Amaster\">\n <img src=\"https://img.shields.io/github/actions/workflow/status/airbus/scikit-decide/ci.yml?branch=master&logo=github&label=CI%20status\" alt=\"actions status\">\n </a>\n <a href=\"https://github.com/airbus/scikit-decide/tags\">\n <img src=\"https://img.shields.io/github/tag/airbus/scikit-decide.svg?label=current%20version\" alt=\"version\">\n </a>\n <a href=\"https://github.com/airbus/scikit-decide/stargazers\">\n <img src=\"https://img.shields.io/github/stars/airbus/scikit-decide.svg\" alt=\"stars\">\n </a>\n <a href=\"https://github.com/airbus/scikit-decide/network\">\n <img src=\"https://img.shields.io/github/forks/airbus/scikit-decide.svg\" alt=\"forks\">\n </a>\n</p>\n<br>\n\n# Scikit-decide for Python\n\nScikit-decide is an AI framework for Reinforcement Learning, Automated Planning and Scheduling.\n\nThis framework was initiated at [Airbus](https://www.airbus.com) AI Research and notably received contributions through the [ANITI](https://aniti.univ-toulouse.fr/en/) and [TUPLES](https://tuples.ai/) projects, and also from [ANU](https://www.anu.edu.au/).\n\n## Main features\n\n<!--features-list-start-->\n\n- **Problem solving:** describe your decision-making problem once and auto-match compatible solvers.\\\n _For instance planning/scheduling problems can be solved by RL solvers using GNNs._\n- **Growing catalog:** enjoy a growing list of domains & solvers catalog, supported by the community.\n- **Open & Extensible:** scikit-decide is open source and is able to wrap existing state-of-the-art domains/solvers.\n- **Domains available:**\n - [Gym(nasium)](https://gymnasium.farama.org/) environments for reinforcement learning (RL)\n - [PDDL](https://planning.wiki/) (Planning Domain Definition Language) via [unified-planning](https://github.com/aiplan4eu/unified-planning) and [plado](https://github.com/massle/plado) libraries\n - encoding in gym(nasium) spaces compatible with RL\n - graph representations for RL (inspired by [Lifted Learning Graph](https://doi.org/10.1609/aaai.v38i18.29986)) :new:\n - [RDDL](https://users.cecs.anu.edu.au/~ssanner/IPPC_2011/RDDL.pdf) (Relational Dynamic Influence Diagram Language) using [pyrddl-gym](https://github.com/pyrddlgym-project) library.\n - Flight planning, based on [openap](https://openap.dev/) or in-house Poll-Schumann for performance model\n - Scheduling, based on rcpsp problem from [discrete-optimization](https://airbus.github.io/discrete-optimization) library\n - Toy domains like: maze, mastermind, rock-paper-scissors\n- **Solvers available:**\n - RL solvers from ray.rllib and stable-baselines3\n - existing algos with action masking\n - adaptation of RL algos for graph observation, based on GNNs from [pytorch-geometric](https://pytorch-geometric.readthedocs.io/) :new:\n - autoregressive models with action masking component by component for parametric actions :new:\n - Planning solvers from [unified-planning](https://github.com/aiplan4eu/unified-planning) library\n - RDDL solvers jax and gurobi-based based on pyRDDLGym-jax and pyRDDLGym-gurobi from [pyrddl-gym project](https://github.com/pyrddlgym-project)\n - Search solvers coded in scikit-decide library:\n - A*\n - AO*\n - Improved-LAO*\n - Learning Real-Time A*\n - Best First Width Search\n - Labeled RTDP\n - Multi-Agent RTDP\n - Iterated Width search (IW)\n - Rollout IW (RIW)\n - Partially-Observable Monte Carlo Planning (POMCP)\n - Monte Carlo Tree Search Methods (MCTS)\n - Multi-Agent Heuristic meta-solver (MAHD)\n - Evolution strategy: Cartesian Genetic Programming (CGP)\n - Scheduling solvers from [discrete-optimization](https://airbus.github.io/discrete-optimization),\n - itself wrapping [ortools](https://developers.google.com/optimization), [gurobi](https://www.gurobi.com/),\n [toulbar](https://toulbar2.github.io/toulbar2/#), [minizinc](https://www.minizinc.org/),\n [deap](https://deap.readthedocs.io/) (genetic algorithm), [didppy](https://didppy.readthedocs.io/) (dynamic programming),\n - and coding local search (hill climber, simulated annealing), Large Neighborhood Search (LNS), and\n genetic programming based hyper-heuristic (GPHH)\n- **Tuning solvers hyperparameters**\n - hyperparameters definition\n - automated study with optuna\n\n<!--features-list-end-->\n\n## Installation\n\nQuick version:\n```shell\npip install scikit-decide[all]\n```\nFor more details, see the [online documentation](https://airbus.github.io/scikit-decide/install).\n\n## Documentation\n\nThe latest documentation is available [online](https://airbus.github.io/scikit-decide).\n\n## Examples\n\nSome educational notebooks are available in `notebooks/` folder.\nLinks to launch them online with [binder](https://mybinder.org/) are provided in the\n[Notebooks section](https://airbus.github.io/scikit-decide/notebooks) of the online documentation.\n\nMore examples can be found as Python scripts in the `examples/` folder, showing how to import or define a domain,\nand how to run or solve it. 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