gym-sandbox

• Customize your own gym environment to simulate any RL problem you want to solve.
• Principle: don't introduce any complexity, focus on algo test!
• Similar project: https://github.com/deepmind/pycolab

How to use

1. git clone this repo
2. cd into repo
3. pip install -e .
4. Switch to your algo, and import gym_sandbox ; gym.make("xxx-v0")
   • all xxx env are in env_list.py of root dir.
   • Please do note that now env requires jupyter notebook

Code structure

• env_list.py >> contains all env you can use
• envs >> core code of envs
• test_algos >> demo algos to solve the env, note it's just demo!
• test >> unittests

Philosophy

Divide and Conquer.

• Divide complex real problem into clean focused sub-tasks, and conquer by each.

Pipeline.

• Integrate your solutions of all sub-tasks.

Origin

We want to solve complex games, like StarCraft, SuperMario, etc. However, these games contains many sub-tasks, if we can't solve one, we can't solve the whole. So we must ensure we can solve each sub-task, and then we have the confidence to solve all.

Features

• OpenAI Gym
  • 100% compatible
• Bokeh
  • We use bokeh to render game state, which is clean, intuitive, and
• Multi-agent
  • Inspired by MADDPG

Credit

OpenAI's universe-starter-agent is a very cool project, where there're many good point of engineering design.