gym-2048-extended 2

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

python setup.py install

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.