A 2048 game api for training supervised learning (imitation learning) follow ExpectiMax agent and create your own CNN model

• game2048/: the main package.
  • game.py: the core 2048 Game class.
  • agents.py: the Agent class with instances.
  • displays.py: the Display class with instances, to show the Game state.
  • expectimax/: a powerful ExpectiMax agent by here.
• explore.ipynb: introduce how to use the Agent, Display and Game.
• static/: frontend assets (based on Vue.js) for web app.
• webapp.py: run the web app (backend) demo.
• evaluate.py: evaluate your self-defined agent. test time = 50
• online_train.py: get your own agent's weight.The structure of the model can be found
• CNN_new_141.zip: get your own agent's weight.The structure of the model can be found

• code only tested on linux system (ubuntu 16.04)
• Python 3 (Anaconda 3.6.3 specifically) with numpy and flask

python online_train.py

• you will get your own model taught by ExpectiMax agent

from game2048.agents import Agent

class YourOwnAgent(Agent):

    def step(self):
        '''To define the agent's 1-step behavior given the `game`.
        You can find more instance in [`agents.py`](game2048/agents.py).
        
        :return direction: 0: left, 1: down, 2: right, 3: up
        '''
        direction = some_function(self.game)
        return direction

cd game2048/expectimax
bash configure
make

python webapp.py

The code is under Apache-2.0 License.

Please read here.