Reorganize README.md (#93)

* Reorganize README.md

* push docs change

* quick fix

* update docs

Author: vwxyzjn

.github/workflows/docs.yaml

@@ -1,10 +1,9 @@
-name: ci 
+name: docs 
 on:
   push:
     branches: 
       - master
       - main
-      - documentation-site
 jobs:
   deploy:
     runs-on: ubuntu-latest

README.md

@@ -1,14 +1,13 @@
 # CleanRL (Clean Implementation of RL Algorithms)
 
-[<img src="https://img.shields.io/badge/discord-cleanrl-green?label=Discord&logo=discord&logoColor=ffffff&labelColor=7289DA&color=2c2f33">](https://discord.gg/D6RCjA6sVT)
-[![Meeting Recordings : cleanrl](https://img.shields.io/badge/meeting%20recordings-cleanrl-green?logo=youtube&logoColor=ffffff&labelColor=FF0000&color=282828&style=flat?label=healthinesses)](https://www.youtube.com/watch?v=dm4HdGujpPs&list=PLQpKd36nzSuMynZLU2soIpNSMeXMplnKP&index=2)
-[<img src="https://github.com/vwxyzjn/cleanrl/workflows/build/badge.svg">](
-https://github.com/vwxyzjn/cleanrl/actions)
+<img src="
+https://img.shields.io/github/license/vwxyzjn/cleanrl">
+[![tests](https://github.com/vwxyzjn/cleanrl/actions/workflows/tests.yaml/badge.svg)](https://github.com/vwxyzjn/cleanrl/actions/workflows/tests.yaml)
+[![ci](https://github.com/vwxyzjn/cleanrl/actions/workflows/docs.yaml/badge.svg)](https://github.com/vwxyzjn/cleanrl/actions/workflows/docs.yaml)
+[<img src="https://img.shields.io/discord/767863440248143916?label=discord">](https://discord.gg/D6RCjA6sVT)
 [<img src="https://badge.fury.io/py/cleanrl.svg">](
 https://pypi.org/project/cleanrl/)
-
-
-
+[<img src="https://img.shields.io/youtube/channel/views/UCDdC6BIFRI0jvcwuhi3aI6w?style=social">](https://www.youtube.com/channel/UCDdC6BIFRI0jvcwuhi3aI6w/videos)
 
 
 CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation with research-friendly features. The implementation is clean and simple, yet we can scale it to run thousands of experiments using AWS Batch. The highlight features of CleanRL are:
@@ -24,6 +23,8 @@ CleanRL is a Deep Reinforcement Learning library that provides high-quality sing
 * 🧫 Experiment Management with [Weights and Biases](https://wandb.ai/site)
 * 💸 Cloud Integration with docker and AWS 
 
+You can read more about CleanRL in our [technical paper]((https://arxiv.org/abs/2111.08819)) and [documentation](https://docs.cleanrl.dev/).
+
 Good luck have fun :rocket:
 
 ## Get started
@@ -90,104 +91,32 @@ python cleanrl/ppg_procgen.py --gym-id starpilot
 python cleanrl/ppg_procgen_impala_cnn.py --gym-id starpilot
 ```
 
+You may also use a prebuilt development environment hosted in Gitpod:
+
+[![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg)](https://gitpod.io/#https://github.com/vwxyzjn/cleanrl/tree/gitpod)
 
 ## Algorithms Implemented
 - [x] Deep Q-Learning (DQN)
-    * [dqn.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn.py)
-        * For discrete action space.
-    * [dqn_atari.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py)
-        * For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques.
 - [x] Categorical DQN (C51)
-    * [c51.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51.py)
-        * For discrete action space.
-    * [c51_atari.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_atari.py)
-        * For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques.
-    * [c51_atari_visual.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_atari_visual.py)
-        * Adds return and q-values visulization for `dqn_atari.py`.
 - [x] Proximal Policy Gradient (PPO) 
-    * All of the PPO implementations below are augmented with some code-level optimizations. See https://costa.sh/blog-the-32-implementation-details-of-ppo.html for more details
-    * [ppo.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo.py)
-        * For discrete action space.
-    * [ppo_continuous_action.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_continuous_action.py)
-        * For continuous action space. Also implemented Mujoco-specific code-level optimizations
-    * [ppo_atari.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari.py)
-        * For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques.
 - [x] Soft Actor Critic (SAC)
-    * [sac_continuous_action.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/sac_continuous_action.py)
-        * For continuous action space.
 - [x] Deep Deterministic Policy Gradient (DDPG)
-    * [ddpg_continuous_action.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py)
-        * For continuous action space.
 - [x] Twin Delayed Deep Deterministic Policy Gradient (TD3)
-    * [td3_continuous_action.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/td3_continuous_action.py)
-        * For continuous action space.
 - [x] Apex Deep Q-Learning (Apex-DQN)
-    * [apex_dqn_atari_visual.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/apex_dqn_atari_visual.py)
-        * For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques.
 
 ## Open RL Benchmark
 
-[Open RL Benchmark](https://wandb.ai/cleanrl/cleanrl.benchmark/reports/Open-RL-Benchmark-0-5-0---Vmlldzo0MDcxOA) by [CleanRL](https://github.com/vwxyzjn/cleanrl) is a comprehensive, interactive and reproducible benchmark of deep Reinforcement Learning (RL) algorithms. It uses Weights and Biases to keep track of the experiment data of popular deep RL algorithms (e.g. DQN, PPO, DDPG, TD3) in a variety of games (e.g. Atari, Mujoco, PyBullet, Procgen, Griddly, MicroRTS). The experiment data includes:
-
-- reproducibility info:
-    - [source code](https://app.wandb.ai/cleanrl/cleanrl.benchmark/runs/2jrqfugg/code?workspace=user-costa-huang) and [requirements.txt](https://app.wandb.ai/cleanrl/cleanrl.benchmark/runs/2jrqfugg/files/requirements.txt)
-    - [](https://app.wandb.ai/cleanrl/cleanrl.benchmark/runs/2jrqfugg/code?workspace=user-costa-huang)[hyper-parameters](https://app.wandb.ai/cleanrl/cleanrl.benchmark/runs/2jrqfugg/overview?workspace=user-costa-huang) and [the exact command to reproduce results](https://app.wandb.ai/cleanrl/cleanrl.benchmark/runs/2jrqfugg/overview?workspace=user-costa-huang)
-- metrics:
-    - [training metrics and videos of the agents playing the game](https://app.wandb.ai/cleanrl/cleanrl.benchmark/runs/2jrqfugg?workspace=user-costa-huang)
-    - [system metrics](https://app.wandb.ai/cleanrl/cleanrl.benchmark/runs/2jrqfugg/system?workspace=user-costa-huang) and [logs](https://app.wandb.ai/cleanrl/cleanrl.benchmark/runs/2jrqfugg/logs?workspace=user-costa-huang)
-
-[Open RL Benchmark](https://wandb.ai/cleanrl/cleanrl.benchmark/reports/Open-RL-Benchmark-0-5-0---Vmlldzo0MDcxOA) has over 1000+ experiments including runs from other projects, which is overwhelming to present in a single report. Instead, we present the results in separate reports. Please click on the links below to access them.
-
-- [Atari results](https://wandb.ai/cleanrl/cleanrl.benchmark/reports/Atari--VmlldzoxMTExNTI)
-- [Mujoco results](https://wandb.ai/cleanrl/cleanrl.benchmark/reports/Mujoco--VmlldzoxODE0NjE)
-- [PyBullet results](https://wandb.ai/cleanrl/cleanrl.benchmark/reports/PyBullet-and-Other-Continuous-Action-Tasks--VmlldzoxODE0NzY)
-- [Procgen results](https://wandb.ai/cleanrl/cleanrl.benchmark/reports/Procgen-New--Vmlldzo0NDUyMTg)
-- [Griddly results](https://wandb.ai/griddly/griddly-paper-generalize?workspace=user-costa-huang)
-- [Gym-μRTS results](https://wandb.ai/vwxyzjn/gym-microrts-paper/reports/Gym-RTS-Toward-Affordable-Deep-Reinforcement-Learning-Research-in-Real-time-Strategy-Games--Vmlldzo2MDIzMTg)
-- [Slimevolleygym results](https://wandb.ai/cleanrl/cleanrl.benchmark/reports/Slimevolleygym--Vmlldzo0ODA1MjA)
-- [PySC2 results](https://wandb.ai/cleanrl/cleanrl.benchmark/reports/Gym-pysc2-Benchmark--VmlldzoyNTEyMTc)
-- [CarRacing-v0](https://wandb.ai/cleanrl/cleanrl.benchmark/reports/CarRacing-v0--VmlldzoyNDUwMzU)
-- [Montezuma Revenge results](https://wandb.ai/cleanrl/cleanrl.benchmark/reports/Montezuma-Revenge--Vmlldzo1MDYxNTk)
-
-
-We hope it could bring a new level of transparency, openness, and reproducibility. Our plan is to 
-benchmark as many algorithms and games as possible. If you are interested, please join us and contribute
-more algorithms and games. To get started, check out our [contribution guide](https://github.com/vwxyzjn/cleanrl/blob/master/CONTRIBUTING.md) and our [roadmap for the Open RL Benchmark](https://github.com/vwxyzjn/cleanrl/projects/1)
-
+CleanRL has a sub project called Open RL Benchmark (https://benchmark.cleanrl.dev/), where we have tracked thousands of experiments across domains. The benchmark is interactive, and researchers can easily query information such as GPU utilization and videos of an agent's gameplay that are normally hard to acquire in other RL benchmarks. Here are some screenshots.
 
-## Cloud integration
-
-Check out the documentation [here](https://github.com/vwxyzjn/cleanrl/tree/master/cloud)
+![](docs/static/o2.png)
+![](docs/static/o3.png)
+![](docs/static/o1.png)
 
 
 ## Support and get involved
 
 We have a [Discord Community](https://discord.gg/D6RCjA6sVT) for support. Feel free to ask questions. Posting in [Github Issues](https://github.com/vwxyzjn/cleanrl/issues) and PRs are also welcome. Also our past video recordings are available at [YouTube](https://www.youtube.com/watch?v=dm4HdGujpPs&list=PLQpKd36nzSuMynZLU2soIpNSMeXMplnKP&index=2)
 
-<!-- In addition, we also have a monthly development cycle to implement new RL algorithms. Feel free to participate or ask questions there, too. You can sign up for our mailing list at our [Google Groups](https://groups.google.com/forum/#!forum/rlimplementation/join) to receive event RVSP which contains the Hangout video call address every week.  -->
-
-## Contribution
-
-We have a short contribution guide here https://github.com/vwxyzjn/cleanrl/blob/master/CONTRIBUTING.md. Consider adding new algorithms 
-or test new games on the Open RL Benchmark (https://benchmark.cleanrl.dev)
-
-Big thanks to all the contributors of CleanRL!
-
-## References
-
-I have been heavily inspired by the many repos and blog posts. Below contains a incomplete list of them.
-
-* http://inoryy.com/post/tensorflow2-deep-reinforcement-learning/
-* https://github.com/seungeunrho/minimalRL
-* https://github.com/Shmuma/Deep-Reinforcement-Learning-Hands-On
-* https://github.com/hill-a/stable-baselines
-
-The following ones helped me a lot with the continuous action space handling:
-
-* https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail
-* https://github.com/zhangchuheng123/Reinforcement-Implementation/blob/master/code/ppo.py
-
-
 ## Citing CleanRL
 
 If you use CleanRL in your work, please cite our technical [paper](https://arxiv.org/abs/2111.08819):
@@ -197,6 +126,8 @@ If you use CleanRL in your work, please cite our technical [paper](https://arxiv
     title={CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms}, 
     author={Shengyi Huang and Rousslan Fernand Julien Dossa and Chang Ye and Jeff Braga},
     year={2021},
-    journal={arXiv preprint arXiv:2111.08819},
-    url={https://arxiv.org/abs/2111.08819}
+    eprint={2111.08819},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
 }
+```

docs/index.md

@@ -1,26 +1,29 @@
 # CleanRL
 
-[<img src="https://img.shields.io/badge/discord-cleanrl-green?label=Discord&logo=discord&logoColor=ffffff&labelColor=7289DA&color=2c2f33">](https://discord.gg/D6RCjA6sVT)
-[![Meeting Recordings : cleanrl](https://img.shields.io/badge/meeting%20recordings-cleanrl-green?logo=youtube&logoColor=ffffff&labelColor=FF0000&color=282828&style=flat?label=healthinesses)](https://www.youtube.com/watch?v=dm4HdGujpPs&list=PLQpKd36nzSuMynZLU2soIpNSMeXMplnKP&index=2)
-[<img src="https://github.com/vwxyzjn/cleanrl/workflows/build/badge.svg">](
-https://github.com/vwxyzjn/cleanrl/actions)
+<img src="
+https://img.shields.io/github/license/vwxyzjn/cleanrl">
+[![tests](https://github.com/vwxyzjn/cleanrl/actions/workflows/tests.yaml/badge.svg)](https://github.com/vwxyzjn/cleanrl/actions/workflows/tests.yaml)
+[![ci](https://github.com/vwxyzjn/cleanrl/actions/workflows/docs.yaml/badge.svg)](https://github.com/vwxyzjn/cleanrl/actions/workflows/docs.yaml)
+[<img src="https://img.shields.io/discord/767863440248143916?label=discord">](https://discord.gg/D6RCjA6sVT)
 [<img src="https://badge.fury.io/py/cleanrl.svg">](
 https://pypi.org/project/cleanrl/)
+[<img src="https://img.shields.io/youtube/channel/views/UCDdC6BIFRI0jvcwuhi3aI6w?style=social">](https://www.youtube.com/channel/UCDdC6BIFRI0jvcwuhi3aI6w/videos)
 
 ## Overview
 
 CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation with research-friendly features. The implementation is clean and simple, yet we can scale it to run thousands of experiments using AWS Batch. The highlight features of CleanRL are:
 
 
-* Single-file Implementation <br>
-**Every detail about an algorithm is put into the algorithm's own file.** Therefore, it's easier for you to fully understand an algorithm and do research with it.
-* Benchmarked Implementation <br>
-  [Details](https://benchmark.cleanrl.dev) on 7+ algorithms and 34+ games 
+* Single-file Implementation
+  * **Every detail about an algorithm is put into the algorithm's own file.** Therefore, it's easier for you to fully understand an algorithm and do research with it.
+* Benchmarked Implementation on 7+ algorithms and 34+ games 
 * Tensorboard Logging
 * Local Reproducibility via Seeding
 * Videos of Gameplay Capturing
 * Experiment Management with [Weights and Biases](https://wandb.ai/site)
-* [Cloud Integration](cloud.md) with Docker and AWS 
+* Cloud Integration with Docker and AWS 
+
+You can read more about CleanRL in our [technical paper]((https://arxiv.org/abs/2111.08819)) and [documentation](https://docs.cleanrl.dev/).
 
 Good luck have fun 🚀