mdplib

A Python library of algorithms for solving infinite-horizon, discounted Markov Decision Processes (MDPs), with a performance-critical core written in C++ (via pybind11).

Implements:

Value Iteration (Jacobi and Gauss-Seidel variants)
Policy Iteration
Q-Learning and SARSA (model-free reinforcement learning)

Installation

pip install mdplib

Prebuilt wheels are published for Linux, Windows and macOS (Python 3.10-3.14), so this does not require a C++ compiler on your machine. If your platform/Python version has no matching wheel, pip falls back to building from source, which does require a C++17 compiler (the extension is compiled at install time):

git clone https://github.com/LuccaGiannelli/mdplib.git
cd mdplib
pip install .

Basic usage

from mdp import MDP

S = [0, 1, 2]   # states
A = [0, 1, 2]   # actions

def Pt(sn, a, sn1):
    # returns P(sn1 | sn, a)
    ...

def rt(sn, a, sn1):
    # returns the reward
    ...

problem = MDP(S, A, Pt, rt)

# Value Iteration
problem.VI(gamma=0.9, tol=1e-6)
problem.print_results()

# Policy Iteration
problem.PI(gamma=0.9, tol=1e-6)
problem.print_results()

See examples/basic_example.py for a complete example (a machine maintenance problem).

Available algorithms

Method	Description
`VI`	Value Iteration (Jacobi)
`VIGS`	Value Iteration with Gauss-Seidel
`PI`	Policy Iteration
`QLearning`	Q-Learning (off-policy TD control)
`SARSA`	SARSA (on-policy TD control)

Common parameters

Parameter	Description
`gamma`	Discount factor (0 ≤ γ < 1)
`tol`	Convergence tolerance
`max_iter`	Maximum number of iterations
`v0`	Initial value vector (optional)
`verbose`	Print progress on completion
`norm_type`	0 = relative sup norm, 1 = sup norm, 2 = euclidean norm
`epsilon`	Final epsilon for the epsilon-greedy policy (Q-Learning / SARSA)
`alpha`	Learning rate (Q-Learning / SARSA)

How it works

You define the state space S, action space A, a transition probability function Pt(sn, a, sn1), and a reward function rt(sn, a, sn1). The constructor enumerates all feasible transitions into a CSV (sn,a,sn1,prob,reward) and loads it into a NumPy array; the algorithms then hand that array to the C++ core, which does the inner loops with direct memory access for performance.

After solving, results are available as:

Attribute	Description
`value`	optimal value for each state
`policy`	optimal action for each state
`time`	execution time (seconds)
`n_iterations`	number of iterations to convergence

Adding a new algorithm

Create mdp/algorithms/new_algo.py with a function def NewAlgo(self, ...):
Import it in mdp/algorithms/__init__.py
Attach it to the class in mdp/core.py: NewAlgo = NewAlgo

Tests

pip install pytest
pytest tests/

License

MIT

Development Status
- 4 - Beta
Operating System
Programming Language
- Python :: 3

Metadata

Development Status
- 4 - Beta
Operating System
Programming Language
- Python :: 3

Release files for mdplib 1.0.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mdplib 1.0.4
File	Size	Uploaded
mdplib-1.0.4.tar.gz	13.1 kB	Oct 6, 2026	Details

Built distributions (wheels)

Table of built distributions (wheels) for mdplib 1.0.4
File	Interpreter	ABI	Platform	Reset
mdplib-1.0.4-cp314-cp314-win_amd64.whl 308.2 kB Oct 6, 2026	CPython 3.14	CPython 3.14	Windows x86-64	Details
mdplib-1.0.4-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl 1.5 MB Oct 6, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64	Details
mdplib-1.0.4-cp314-cp314-macosx_14_0_arm64.whl 112.6 kB Oct 6, 2026	CPython 3.14	CPython 3.14	macOS 14.0+ ARM64	Details
mdplib-1.0.4-cp313-cp313-win_amd64.whl 298.4 kB Oct 6, 2026	CPython 3.13	CPython 3.13	Windows x86-64	Details
mdplib-1.0.4-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl 1.5 MB Oct 6, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64	Details
mdplib-1.0.4-cp313-cp313-macosx_14_0_arm64.whl 112.9 kB Oct 6, 2026	CPython 3.13	CPython 3.13	macOS 14.0+ ARM64	Details
mdplib-1.0.4-cp312-cp312-win_amd64.whl 298.3 kB Oct 6, 2026	CPython 3.12	CPython 3.12	Windows x86-64	Details
mdplib-1.0.4-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl 1.5 MB Oct 6, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64	Details
mdplib-1.0.4-cp312-cp312-macosx_14_0_arm64.whl 112.8 kB Oct 6, 2026	CPython 3.12	CPython 3.12	macOS 14.0+ ARM64	Details
mdplib-1.0.4-cp311-cp311-win_amd64.whl 295.5 kB Oct 6, 2026	CPython 3.11	CPython 3.11	Windows x86-64	Details
mdplib-1.0.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl 1.4 MB Oct 6, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64	Details
mdplib-1.0.4-cp311-cp311-macosx_14_0_arm64.whl 109.8 kB Oct 6, 2026	CPython 3.11	CPython 3.11	macOS 14.0+ ARM64	Details
mdplib-1.0.4-cp310-cp310-win_amd64.whl 295.0 kB Oct 6, 2026	CPython 3.10	CPython 3.10	Windows x86-64	Details
mdplib-1.0.4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl 1.4 MB Oct 6, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64	Details
mdplib-1.0.4-cp310-cp310-macosx_14_0_arm64.whl 108.6 kB Oct 6, 2026	CPython 3.10	CPython 3.10	macOS 14.0+ ARM64	Details

Total release size: 9.4 MB

Release files / mdplib-1.0.4.tar.gz

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Size	1.4 MB
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Release files / mdplib-1.0.4-cp310-cp310-macosx_14_0_arm64.whl

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Release history Release notifications | RSS feed

This release

1.0.4 This release

Oct 6, 2026 16 release files

1.0.2

Sep 16, 2026 16 release files

1.0.0

Sep 1, 2026 16 release files

mdplib 1.0.4

mdplib

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