jidi_ai

• Python 3.7.5

• gym https://github.com/openai/gym

• gfootball https://github.com/google-research/football

  (If want to use football_5v5_malib, put the env/football_scenarios/malib_5_vs_5.py file under folder like ~/anaconda3/envs/env_name/lib/python3.x/site-packages/gfootball/scenariosusing environment env_name or ~/anaconda3/lib/python3.x/site-packages/gfootball/scenarios using base environment.)

• miniworld https://github.com/maximecb/gym-miniworld#installation

• minigrid https://github.com/maximecb/gym-minigrid

• Multi-Agent Particle Environment https://www.pettingzoo.ml/mpe

  pip install pettingzoo[mpe]==1.10.0

  (Using pip install 'pettingzoo[mpe]==1.10.0' if you are using zsh.)

• Overcooked-AI https://github.com/HumanCompatibleAI/overcooked_ai

• MAgent https://www.pettingzoo.ml/magent

  (Using pip install 'pettingzoo[magent]' if you are using zsh; Using render_from_log.py for MAgent local render)

• SMARTS https://gitee.com/mirrors_huawei-noah/SMARTS

  (Put repo SMARTS and ai_lib under the same folder.

  If not using smarts, comment out from .smarts_jidi import * in env/__init__.py.

  If want to use NGSIM scenario, download NGSIM scenario here: https://www.dropbox.com/sh/fcky7jt49x6573z/AADUmqmIXhz_MfBcenid43hqa/ngsim?dl=0&subfolder_nav_tracking=1 and put the ngsim folder under SMARTS/scenarios.

  If not using smarts NGSIM, comment out from .smarts_ngsim import * in env/__init__.py.)

• StartCraft II https://github.com/deepmind/pysc2

• olympics-running https://github.com/jidiai/Competition_Olympics-Running

  (Notice: Put folder olympics and jidi under the same folder)

• olympics-tablehockey olympics-football olympics-wrestling https://github.com/jidiai/olympics_engine

  (Notice: Put repo olympics_engine and jidi under the same folder)

• mujoco-py https://github.com/openai/mujoco-py

• Classic https://www.pettingzoo.ml/classic

  pip install pettingzoo[classic]==1.10.0

  (Using pip install 'pettingzoo[classic]==1.10.0' if you are using zsh.)

• gym-chinese-chess https://github.com/bupticybee/gym_chinese_chess

• Wilderness Scavenger https://github.com/inspirai/wilderness-scavenger

• REVIVE SDK https://www.revive.cn/help/polixir-revive-sdk/text/introduction.html

• Torch 1.7.0 可选

  • 支持提交Torch训练后的模型.pth附属文件

|-- platform_lib
	|-- README.md
	|-- run_log.py		// 本地调试运行环境
	|-- examples	// 提交运行文件示例	需包含 my_controller 函数输出policy
	    |-- random.py  // 随机策略 需根据环境动作是否连续 调整 is_act_continuous 的值
	|-- replay		// render工具，用于非gym环境，打开replay.html上传run_log 存储的.json文件 
	|-- env		// 游戏环境 
	|	|-- simulators		// 模拟器
	|	|	|-- game.py
	|	|	|-- gridgame.py // 网格类模拟器接口
	|	|-- obs_interfaces		// observation 观测类接口
	|	|	|-- observation.py		// 目前支持Grid Vector
	|	|-- config.ini		// 相关配置文件
	|	|-- chooseenv.py 
	|	|-- snakes.py
	|	|-- gobang.py
	|	|-- reversi.py
	|	|-- sokoban.py
	|	|-- ccgame.py

1. 填写算法名称或描述，选择提交环境
2. 上传一个或多个文件。

• 其中必须包含一个运行文件，运行文件需包含my_controller 函数的一个submission.py文件。
• 附属文件支持.pth .py类型文件。大小不超过100M，个数不超过5个。