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If you have any suggestion about ElegantRL Helloworld, you can discuss them here, and we will keep an eye on this issue.
ElegantRL's code, especially the Helloworld, really needs a lot of feedback to be better.
如果大家对ElegantRL 的 Helloworld 有什么意见，可以在此处讨论，我们会持续关注这个问题。
ElegantRL 的代码，尤其是 Helloworld 真的需要很多人的反馈，才能改好。
We hope that the ElegantRL Helloworld would help people who want to learn about reinforcement learning to quickly run a few introductory examples.
Based on previous feedback, we've come up with a few requirements for the Helloworld code, with reasons in parentheses, to discuss with you.
- Less lines of code. (In order to allow the reader to read it in 10 minutes, we want the code lines <1000)
- Less packages requirements. (To reduce the installation time and difficulty of the code, only
torchfor deep learning, andgymgym[Box2D]` for RL learning environment should be installed) - have a gentle learning curve for reader.. (The reader can learn about reinforcement learning through the code, or learn about the core code of ElegantRL, so we show the DRL algorithm in order:
DQN -> DDPG -> PPO) - Helloworld code should be in a consistent style with the full version of ElegantRL. (To help the reader's understanding of
ElegantRLthroughElegantRL_helloworld)
我们想要改写 ElegantRL 的 Helloworld 代码，希望它能帮助想要了解 强化学习的人快速运行几个入门例子。
根据大家的反馈，我们提出了对 Helloworld 代码的几点要求，并在括号内给出原因，同大家讨论：
- 代码行数要少。（为了方便读者能在10分钟内读完，helloworld版本的代码量将维持在1000行以内）
- 代码依赖的安装的包要足够少。（减少代码的安装时间，降低安装难度，只需要安装深度学习必备的
torch, 以及标准学习环境gym，可选的gym[Box2D]） - 代码要有平缓的学习曲线。（读者可以通过代码了解强化学习，或者了解ElegantRL的核心代码，所以我们按顺序展示了DRL算法：
DQN -> DDPG -> PPO） - 代码要与 ElegantRL 完整版的代码 保持统一的风格。（方便读者通过
ElegantRL_helloworld了解ElegantRL）