A 2048 game api for training supervised learning (imitation learning) or reinforcement learning agents
game2048/
: the main package.game.py
: the core 2048Game
class.agents.py
: theAgent
class with instances.displays.py
: theDisplay
class with instances, to show theGame
state.expectimax/
: a powerful ExpectiMax agent by here.RNN.py
:RNN netNet.py
:CNN nettrainRNN.py
:offline trainzaixian2048.py
:online trainzaixian256.py
:online train
explore.ipynb
: introduce how to use theAgent
,Display
andGame
.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.
- code only tested on linux system (ubuntu 16.04)
- Python 3 (Anaconda 3.6.3 specifically) with numpy and flask
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
- run trainRNN.py or zaixian2048.py or zaixian256.py to obtain model
- run evaluate.py to get score
- testAgent can be changed
- MyAgent1:single model
- MyAgent2:multiple models to vote
- MyAgent3:2 models individually used under 258 and above 256
cd game2048/expectimax
bash configure
make
python webapp.py
The code is under Apache-2.0 License.
Please read course project requirements and description.