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ElegantRL is developed for practitioners with the following advantages:

• Cloud-native: follows a cloud-native paradigm through microservice architecture and containerization, supporting ElegantRL-Podracer and FinRL-Podracer.

• Scalable: fully exploits the parallelism of DRL algorithms at multiple levels, making it easily scale out to hundreds or thousands of computing nodes on a cloud platform, say, a DGX SuperPOD platform with thousands of GPUs.

• Elastic: allows to elastically and automatically allocate computing resources on the cloud.

• Lightweight: the core codes <1,000 lines (check Elegantrl_Helloworld).

• Efficient: in many testing cases (single GPU/multi-GPU/GPU cloud), we find it more efficient than Ray RLlib.

• Stable: much much much more stable than Stable Baselines 3 by utilizing various ensemble methods.

ElegantRL implements the following model-free deep reinforcement learning (DRL) algorithms:

• DDPG, TD3, SAC, PPO, REDQ for continuous actions,
• DQN, Double DQN, D3QN, SAC for discrete actions,
• QMIX, VDN, MADDPG, MAPPO, MATD3 for multi-agent environment.

For the details of DRL algorithms, please check out the educational webpage OpenAI Spinning Up.

ElegantRL supports the following simulators:

• Isaac Gym for massively parallel simulation,
• OpenAI Gym, MuJoCo, PyBullet, FinRL for benchmarking.

“小雅”源于《诗经·小雅·鹤鸣》，旨在「他山之石，可以攻玉」。

• News
• ElegantRL-Helloworld
• File Structure
• Experimental Demos
• Requirements
• Citation

• [Towardsdatascience] ElegantRL-Podracer: A Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning, Dec 11, 2021.
• [Towardsdatascience] ElegantRL: Mastering PPO Algorithms, May 3, 2021.
• [MLearning.ai] ElegantRL Demo: Stock Trading Using DDPG (Part II), Apr 19, 2021.
• [MLearning.ai] ElegantRL Demo: Stock Trading Using DDPG (Part I), Mar 28, 2021.
• [Towardsdatascience] ElegantRL-Helloworld: A Lightweight and Stable Deep Reinforcement Learning Library, Mar 4, 2021.

For beginners, we maintain ElegantRL-Helloworld as a tutorial. Its goal is to get hands-on experience with ELegantRL.

One sentence summary: an agent (agent.py) with Actor-Critic networks (net.py) is trained (run.py) by interacting with an environment (env.py).

• elegantrl # main folder
  • envs, # a collection of environments
  • agent.py, # DRL algorithms
  • config.py, # configurations (hyper-parameter)
  • demo.py, # a collection of demos
  • evaluator.py # the evaluator class
  • net.py # a collection of network architectures
  • replay_buffer.py # the buffer class
  • run.py # training loop
• elegantrl_helloworld # tutorial version
  • env.py,
  • agent.py,
  • demo.py,
  • net.py
  • run.py
• examples # a collection of example codes
• ready-to-run Google-Colab notebooks
  • quickstart_Pendulum_v1.ipynb
  • tutorial_BipedalWalker_v3.ipynb
  • tutorial_Creating_ChasingVecEnv.ipynb
  • tutorial_LunarLanderContinuous_v2.ipynb

Experiments on Ant (MuJoCo), Humainoid (MuJoCo), Ant (Isaac Gym), Humanoid (Isaac Gym) # from left to right

ElegantRL fully supports Isaac Gym that runs massively parallel simulation (e.g., 4096 sub-envs) on one GPU.

Experiment on Hopper-v2 # ElegantRL achieves higher average rewards and much smaller variance (average over 10 runs)

Necessary:
| Python 3.6+     |           
| PyTorch 1.6+    |    

Not necessary:
| Numpy 1.18+     | For ReplayBuffer. Numpy will be installed along with PyTorch.
| gym 0.17.0      | For env. Gym provides tutorial env for DRL training. (env.render() bug in gym==0.18 pyglet==1.6. Change to gym==0.17.0, pyglet==1.5)
| pybullet 2.7+   | For env. We use PyBullet (free) as an alternative of MuJoCo (not free).
| box2d-py 2.3.8  | For gym. Use pip install Box2D (instead of box2d-py)
| matplotlib 3.2  | For plots. 

pip3 install gym==0.17.0 pybullet Box2D matplotlib

To install StarCraftII env,
bash ./elegantrl/envs/installsc2.sh
pip install -r sc2_requirements.txt

To cite this repository:

@misc{erl,
  author = {Liu, Xiao-Yang and Li, Zechu and Wang, Zhaoran and Zheng, Jiahao},
  title = {{ElegantRL}: Massively Parallel Framework for Cloud-native Deep Reinforcement Learning},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/AI4Finance-Foundation/ElegantRL}},
}