This repo provides a reinforcement learning library designed for our online submission evaluation platform Jidi(及第) which aims to offer fair benchmarking over various RL environments and host AI competitions on problems worth exploring.

Version 2.0 (branch V2) New version of Jidi AiLib which supports Population-Based Training! Still under construction and stay tuned. PRs are welcome!

Version 1.0 (branch master): In the version V1.0, the repo contains the code of all the benchmark RL environment we included on Jidi(及第) and simple-to-use training examples covering some classic baseline RL algorithms.

jidi_ai

You can install Jidi AiLib on your own personal computer or workstation. To install it, please follow the instruction below.

Clone the repo

git clone https://github.com/jidiai/ai_lib.git
cd ailib

Build virtual environment

python3 -m venv ailib-venv
source ailib-venv/bin/activate
python3 -m pip install -e .

or

conda create -n ailib-venv python==3.7.5
conda activate ailib-venv

Install necessary dependencies

pip install -r requirements.txt

Now have a go

python examples/main.py --scenario classic_CartPole-v0 --algo dqn --reload_config 

We provide implementations and tuned training configurations of vairous baseline reinforcement learning algorithms. More details can be found in ./examples/. Feel free to try it yourself.

We currently support the following benchmarking experiments:

Apart from the necessary dependency in requirements.txt, some environments require extra dependencies and we list all of them here.

• 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]==1.12.0

  (Using pip install 'pettingzoo[mpe]==1.12.0' 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]==1.12.0

• pip install rlcard==1.0.4

  (Using pip install 'pettingzoo[classic]==1.12.0' 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

• FinRL https://github.com/AI4Finance-Foundation/FinRL

  pip install git+https://github.com/AI4Finance-Foundation/FinRL.git

• TaxingAI: clone the branch git clone --branch jidi_version https://github.com/jidiai/TaxAI.git

• 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。