matrix-mdp-gym 1.1.1

An OpenAI gym / Gymnasium environment to seamlessly create discrete MDPs from matrices.

Matrix MDP

Easily generate an MDP from transition and reward matricies.

Want to learn more on the story behind this repo? Check the blog post here!

Installation

Assuming you are in the root directory of the project, run the following command:

pip install matrix-mdp-gym

Usage

import gymnasium as gym
import matrix_mdp
env = gym.make('matrix_mdp/MatrixMDP-v0')

Environment documentation

Description

A flexible environment to have a gym API for discrete MDPs with N_s states and N_a actions given:

• A vector of initial state distribution vector P_0(S)
• A transition probability matrix P(S' | S, A)
• A reward matrix R(S', S, A) of the reward for reaching S' after having taken action A in state S

Action Space

The action is a ndarray with shape (1,) representing the index of the action to execute.

Observation Space

The observation is a ndarray with shape (1,) representing index of the state the agent is in.

Rewards

The reward function is defined according to the reward matrix given at the creation of the environment.

Starting State

The starting state is a random state sampled from $P_0$.

Episode Truncation

The episode truncates when a terminal state is reached. Terminal states are inferred from the transition probability matrix as $\sum_{s' \in S} \sum_{s \in S} \sum_{a \in A} P(s' | s, a) = 0$

Arguments

• p_à: ndarray of shape (n_states, ) representing the initial state probability distribution.
• p: ndarray of shape (n_states, n_states, n_actions) representing the transition dynamics $P(S' | S, A)$.
• r: ndarray of shape (n_states, n_states, n_actions) representing the reward matrix.

import gymnasium as gym
import matrix_mdp

gym.make('MatrixMDP-v0', p_0=p_0, p=p, r=r)

Version History

• v0: Initial versions release

Acknowledgements

Thanks to Will Dudley for his help on learning how to put a Python package together/