gym-2048-extended 2.4

OpenAI Gym Environment for 2048 extended functionality

This package implements the classic grid game 2048 for OpenAI gym environment.

Summarizing my changes to the repo Activated Geek Gym 2048:

• changed the requirements.txt to be more flexible

• added the method is_action_possible

• added different reward schemes

• added the render mode dict

• added game.py a PyGame that uses the GymEnvironment from this repo

Install

Pip

pip install gym-2048-extended

From cloned repository

GitHub Page

python setup.py install

C:UsersUserAppDataLocalProgramsPythonPython310Libsite-packages

Environment(s)

The package currently contains two environments

• Tiny2048-v0: A 2 x 2 grid game.

• 2048-v0: The standard 4 x 4 grid game.

I only checked the 4 x 4 grid game, the other one might not work.

Attributes

• Observation: All observations are n x n numpy arrays representing the grid. The array is 0 for empty locations and numbered 2, 4, 8, ... wherever the tiles are placed.

• Actions: There are four actions defined by integers.

  • LEFT = 0

  • UP = 1

  • RIGHT = 2

  • DOWN = 3

• Reward: Reward is the total score obtained by merging any potential tiles for a given action. Score obtained by merging two tiles is simply the sum of values of those two tiles.

Rendering

Currently 2 rendering modes are implemented

• basic print rendering (mode='human')

• dict rendering (mode='dict') returns a dictionary with the board state

Usage

PyGame Interactive Demo

game.py provides a PyGame implementation of the game. Use the arrow keys to play, q and n can be used to quit the game or restart it.

The game serves as a demo, the different reward schemes and step function can be explored.

python gym_2048/game.py

Basic Demo

Here is a sample rollout of the game which follows the same API as OpenAI gym.Env.

import gym_2048
import gym


if __name__ == '__main__':
  env = gym.make('2048-extended-v2')
  env.seed(42)

  env.reset()
  env.render()

  done = False
  moves = 0
  while not done:
    action = env.np_random.choice(range(4), 1).item()
    next_state, reward, done, _, info = env.step(action)
    moves += 1

    print('Next Action: "{}"\n\nReward: {}'.format(
      gym_2048.Base2048Env.ACTION_STRING[action], reward))
    env.render()

  print('\nTotal Moves: {}'.format(moves))

NOTE: Top level import gym_2048 is needed to ensure registration with Gym.