gymnasium-sudoku 0.3.1

A Sudoku environment for Reinforcement Learning research

pip install gymnasium_sudoku

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]]

Horizon: This parameter controls the number of steps after which Truncated is set to True and the environment is reset. Otherwise, early in training (when the policy is still mostly random and the exploration incentive is high), the policy may corrupt the board and either make it unsolvable or push it into a local minimum. The default value for this parameter is set to 400 for no specific reason and should probably be adjusted during initialization.

Eval mode/Training mode : By default, eval_mode in the init method is set to False. This is used for training, where the environment is reset with one of 50 different boards after eacg .reset() call. During testing, eval_mode should be set to True in order to evaluate the agent on boards that were never seen during the training phase.

Sudoku-v0 (biased version)

import gymnasium as gym

env = gym.make("sudoku-v0",mode="biased"render_mode="human",horizon=600,eval_mode=True)
env.reset() 

for n in range(int(6e3)):
    env.step(env.action_space.sample())
    env.render() 

Bias : 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.

Measuring learning for this version of the environment:* 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.

Sudoku-v1

import gymnasium as gym

env = gym.make("sudoku-v1",mode="easy",render_mode="human",horizon=600,eval_mode=True)
env.reset() 

for n in range(int(6e3)):
    env.step(env.action_space.sample())
    env.render()