Scikit-decide

# Guide

# Introduction

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

It is meant for being a one-stop shop solution to formalize decision-making problems, finding compatible solvers among a growing catalog and get the best solution possible. The catalog is a combination of wrapped existing domains/solvers and newly contributed ones.

Architecture

Please refer to our installation instructions for installing scikit-decide.

# As a domain developer

TIP

Scikit-decide supports formalizing the problem one characteristic at a time without the need of being an algorithmic expert nor knowing in advance the best kind of solver for this task (RL, planning, scheduling or any hybrid type).

# As a solver developer

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Scikit-decide provides a meaningful API to interact with domains at the expected level of information, as well as a catalog of domains/solvers to test/benchmark new algorithms.

# Getting started

Domain characteristics are one of the key concepts in scikit-decide: they are combined on the one hand to define domains, on the other hand to specify the envelope of domains a solver can tackle.

Each characteristic has various levels, from general (high-level) to specialized (low-level) ones, each level inheriting higher-level functions. Any domain fully contained in a solver's envelope is compatible with this solver, unless it violates additional requirements (optional).

Characteristics

Defining a domain to solve is a matter of:

  • selecting a base domain class (Domain by default or any pre-made template for typical combinations like DeterministicPlanningDomain)
  • fine-tuning any necessary characteristic level with something more specialized (lower-level)
  • auto-generating the code skeleton from the combination above (technically by implementing all abstract methods)
  • filling the code as needed based on domain expertise

TIP

When starting a new domain or solver, it is highly recommended to check the code generators for assistance and auto-generation of the skeleton to fill.

Check How to to see how to find compatible solvers and compute a solution, once a domain is defined.

# How to

WARNING

Exact prints and outputs may vary depending on which domains/solvers are registered on your system.

# Select a domain to solve

This step can be skipped if a domain has already been defined. Otherwise, here is how to load one from the catalog of registered domains:

from skdecide import utils

print(utils.get_registered_domains())
# prints: ['GymDomain', 'MasterMind', 'Maze', 'RockPaperScissors', ...]

MyDomain = utils.load_registered_domain('Maze')

# Find compatible solvers

This step can be skipped if a solver is already known to be compatible and selected as best candidate. Otherwise, here is how to find all compatible solvers:

compatible_solvers = utils.match_solvers(MyDomain())
print(compatible_solvers)
# prints: [<class 'skdecide.hub.solver.lazy_astar.lazy_astar.LazyAstar'>, ...]

# select Lazy A* solver and instanciate with default parameters
from skdecide.hub.solver.lazy_astar import LazyAstar
mysolver = LazyAstar()

# Compute a solution

Here is how to solve MyDomain with mysolver:

MyDomain.solve_with(mysolver)

# Test the solution

# Simple case (one basic rollout)
utils.rollout(MyDomain(), mysolver)

# Example of additional rollout parameters
utils.rollout(MyDomain(), mysolver, num_episodes=3, max_steps=1000, max_framerate=30)

In the example of the Maze solved with Lazy A*, the goal (in green) should be reached by the agent (in blue):

Maze

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The rendering of the maze is done in a separate window when running in a local python script. To get a similar result in a jupyter notebook, add a line

%matplotlib qt

before calling rollout(). See also the available tutorial notebooks to know how to render the maze inline.

# Clean up the solver

Some solvers (especially parallel C++ ones) need to be properly cleaned once used.

mysolver._cleanup()

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Note that this is automatically done if you use the solver within a with statement:

with LazyAstar() as mysolver:
    MyDomain.solve_with(mysolver)
    utils.rollout(MyDomain(), mysolver)

# Examples

# Notebooks

Go to the dedicated Notebooks page to see a curated list of notebooks recommended to start with scikit-decide.

# Python scripts

More examples can be found 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.

# Playground

The best example to try out scikit-decide capabilities might be examples/full_multisolve.py. This interactive console experience makes it easy to pick a domain among a pre-defined catalog selection:

  • Simple Grid World
  • Maze
  • Mastermind
  • Cart Pole (Gymnasium)
  • Mountain Car continuous (Gymnasium)
  • ATARI Pacman (Gymnasium)

...and then solve it with any compatible solver (detected automatically) among following selection:

  • Random walk
  • Simple greedy
  • Lazy A* (classical planning)
  • PPO: Proximal Policy Optimization (deep reinforcement learning)
  • POMCP: Partially Observable Monte-Carlo Planning (online planning for POMDP)
  • CGP: Cartesian Genetic Programming (evolution strategy)
  • IW: Iterated Width search (width-based planning)

Note: some requirements declared in above solvers still need fine-tuning, so in some cases an auto-detected compatible solver may still be unable to solve a domain (for now).

These combinations are particularly efficient if you want to try them out:

  • Simple Grid World -> Lazy A*
  • Maze -> Lazy A*
  • Mastermind -> POMCP: Partially Observable Monte-Carlo Planning
  • Cart Pole -> PPO: Proximal Policy Optimization
  • Mountain Car continuous -> CGP: Cartesian Genetic Programming
  • ATARI Pacman -> Random walk

WARNING

Some domains/solvers might require extra manual setup steps to work at 100%. In the future, each scikit-decide hub entry might have a dedicated help page to list them, but in the meantime please refer to this:

# Code generators

Go to Code generators for assistance when creating a new domain or solver.

# Roadmap

Following features will be added to scikit-decide soon:

  • Scheduling API
  • PDDL parser