Information
# 2048-api
A 2048 game api for training supervised learning (imitation learning) or reinforcement learning agents
## Code structure
* [\`game2048/\`](game2048/): the main package.
* [\`game.py\`](game2048/game.py): the core 2048 \`Game\` class.
* [\`agents.py\`](game2048/agents.py): the \`Agent\` class with instances.
* [\`displays.py\`](game2048/displays.py): the \`Display\` class with instances, to show the \`Game\` state.
* [\`expectimax/\`](game2048/expectimax): a powerful ExpectiMax agent by [here](https://github.com/nneonneo/2048-ai).
* [\`explore.ipynb\`](explore.ipynb): introduce how to use the \`Agent\`, \`Display\` and \`Game\`.
* [\`static/\`](static/): frontend assets (based on Vue.js) for web app.
* [\`webapp.py\`](webapp.py): run the web app (backend) demo.
* [\`evaluate.py\`](evaluate.py): evaluate your self-defined agent.
## Requirements
* code only tested on linux system (ubuntu 16.04)
* Python 3 (Anaconda 3.6.3 specifically) with numpy and flask
## To define your own agents
\`\`\`python
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
\`\`\`
## To compile the pre-defined [ExpectiMax](https://github.com/nneonneo/2048-ai) agent
\`\`\`bash
cd game2048/expectimax
bash configure
make
\`\`\`
## To run the web app
\`\`\`bash
python webapp.py
\`\`\`
## LICENSE
The code is under Apache-2.0 License.
## For EE369 / EE228 students from SJTU
Please read course project [requirements](EE369.md) and [description](https://docs.qq.com/slide/DS05hVGVFY1BuRVp5).
## Acknowledgement
The wrapped ExpectiMax agent is based on [nneonneo/2048-ai](https://github.com/nneonneo/2048-ai).