# Scikit-Decide AI Framework for RL, Auto Planning and Scheduling

> Source: <https://airbus.github.io/scikit-decide/>
> Published: 2026-09-16 09:26:41+00:00

# Scikit-decide

AI framework for Reinforcement Learning, Automated Planning and Scheduling

## Problem solving

Describe your decision-making problem once and auto-match compatible solvers.

## 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.

# [#](#welcome-to-scikit-decide) Welcome to scikit-decide

 ## [#](#what-is-it) What is it?

 **Scikit-decide is an AI framework for Reinforcement Learning, Automated Planning and Scheduling.**

This framework was initiated at [Airbus (opens new window)](https://www.airbus.com) AI Research and notably received contributions through the [ANITI (opens new window)](https://aniti.univ-toulouse.fr/en/) and [TUPLES (opens new window)](https://tuples.ai/) projects, and also from [ANU (opens new window)](https://www.anu.edu.au/).

TIP

Please refer to the **Guide** and **Reference** sections at the top to learn how to use scikit-decide.

## [#](#main-features) Main features

 - **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) (opens new window)](https://gymnasium.farama.org/) environments for reinforcement learning (RL)
  - [PDDL (opens new window)](https://planning.wiki/) (Planning Domain Definition Language) via[unified-planning (opens new window)](https://github.com/aiplan4eu/unified-planning) and[plado (opens new window)](https://github.com/massle/plado) libraries
    - encoding in gym(nasium) spaces compatible with RL
    - graph representations for RL (inspired by [Lifted Learning Graph (opens new window)](https://doi.org/10.1609/aaai.v38i18.29986) ) 🆕
  - [RDDL (opens new window)](https://users.cecs.anu.edu.au/~ssanner/IPPC_2011/RDDL.pdf) (Relational Dynamic Influence Diagram Language) using[pyrddl-gym (opens new window)](https://github.com/pyrddlgym-project) library.
  - Flight planning, based on [openap (opens new window)](https://openap.dev/) or in-house Poll-Schumann for performance model
  - Scheduling, based on rcpsp problem from [discrete-optimization (opens new window)](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 (opens new window)](https://pytorch-geometric.readthedocs.io/) 🆕
(with sb3 and ray.rllib (old api stack) in last release, only with sb3 on master since migration of ray.rllib to new api stack,
work in progress to do it with ray.rllib + new api stack)
    - autoregressive models with action masking component by component for parametric actions 🆕 (only with sb3 for now, work in progress for ray.rllib + new api stack)
  - Planning solvers from [unified-planning (opens new window)](https://github.com/aiplan4eu/unified-planning) library
  - RDDL solvers jax and gurobi-based based on pyRDDLGym-jax and pyRDDLGym-gurobi from [pyrddl-gym project (opens new window)](https://github.com/pyrddlgym-project)
  - Search solvers coded in scikit-decide library:
    - A*, AO*, Improved-LAO*
    - Value Iteration (VI), Policy Iteration (PI)
    - Labeled RTDP, Learning Real-Time A*
    - LDFS (Label-correcting Depth-First Search), Iterative Deepening A*
    - SSiPP (Short-Sighted Planning), FRET (Find, Revise, Eliminate Traps)
    - iDual (LP-based SSP solver), Goal Probability and Cost Iteration (GPCI)
    - Best First Width Search, Iterated Width (IW), Rollout IW (RIW)
    - Monte Carlo Tree Search (MCTS), POMCP
    - DESPOT, SARSOP, Witness (POMDP solvers)
    - RTDP-Bel (belief-space RTDP), HSVI / GoalHSVI
    - SSPReplan, SSPDetHindsight, SSPPlanMerger (determinization approaches)
    - Multi-Agent RTDP, Multi-Agent Heuristic meta-solver (MAHD)
  - (Probabilistic) PDDL (PPDDL) solvers:
    - FF planner
    - FFReplan / PPDDLReplan (replanning with pluggable inner solvers)
    - FFDetHindsight / PPDDLDetHindsight (determinization in hindsight)
    - RFF / PPDDLPlanMerger (plan aggregation into a policy)
  - PDDL heuristics (with their probabilistic extensions):
    - Delete-Relaxation heuristics
    - FF Heuristic
  - PDDL+ parser and simulators with Probabilistic PDDL extensions
    - Lifted applicable action filtering using Clingo
    - Z3-based event synchronization in python using [z3-solver (opens new window)](https://pypi.org/project/z3-solver/)
  - Evolution strategy: Cartesian Genetic Programming (CGP)
  - Scheduling solvers from [discrete-optimization (opens new window)](https://airbus.github.io/discrete-optimization) ,
    - itself wrapping [ortools (opens new window)](https://developers.google.com/optimization) ,[gurobi (opens new window)](https://www.gurobi.com/) ,[toulbar (opens new window)](https://toulbar2.github.io/toulbar2/#) ,[minizinc (opens new window)](https://www.minizinc.org/) ,[deap (opens new window)](https://deap.readthedocs.io/) (genetic algorithm),[didppy (opens new window)](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)
  - itself wrapping 
- RL solvers from ray.rllib and stable-baselines3
- **Tuning solvers hyperparameters**  - hyperparameters definition
  - automated study with optuna