gymnasium-sudoku 0.2.3

A Sudoku environment for Reinforcement Learning research

pip install gymnasium_sudoku

import gymnasium_sudoku
import gymnasium as gym

env = gym.make("sudoku-v0",render_mode="human",horizon=150,render_delay=1.0,eval_mode=True)
env.reset() 
steps = 100

for n in range(steps):
    env.step(env.action_space.sample())
    env.render() 

And for training mode:

env = gym.make("sudoku-v0",horizon=150,eval_mode=False) # no rendering during  training  

• Observation space : The state returned after each .reset() or .step() is a raw sudoku board shape [9,9].This observation can be converted into an image.

• Action space: The action space is shaped [x,y,z],representing : x = row position of the cell, y = column position of the cell and value that should go into that cell.When vectorizing, the current version of the environment do not handle action reshaping, so for n environments, the action's shape should be : [[x0...xn],[y0...yn],[z0...zn]]

• Eval mode/Training mode : By default, eval_mode is set to False, this is good for training since after each reset() call,the Sudoku board will be changed to add more diversity to the training data and try to prevent memorization, so that the policy learns a more general distribution...At least that is the intuition. During testing, eval_mode should be set to True to test the generalization capabilities of a trained policy or to test the environment with a random policy.This is important; otherwise,when testing a trained policy,it will be tested on states seen during training which would measure memorization rather than generalization.This makes it an invalid test of the policy's true capabilities.

• Measuring learning & Inductive Biases:* The current structure of the environment allows a completely random policy to solve it (this is true for easy boards in the current version of the environment), so a good way to measure learning might be to use the number of steps over N episodes under a random policy as a baseline. This implies that a policy able to consistently solve the test boards in fewer steps over the same N episodes used to run a random policy is, in theory, displaying some sort of learning. Among the induced biases that immensely help guide that learning is the fact that the policy cannot modify a cell that was already correctly filled, on top of the existing untouchable cells present in the beginning. That attribute of the current environment reflects the way broadly available Sudoku environments intended for human players are structured.