ElegantRL: Massively Parallel Deep Reinforcement Learning
ElegantRL is an advanced library developed to facilitate scalable and efficient deep reinforcement learning (DRL) experiments. This project is especially geared towards users and developers seeking to leverage powerful computing resources in cloud-based environments. Here is a detailed look at what makes ElegantRL a distinguished tool in its field:
Key Features
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Cloud-native: ElegantRL is designed with cloud-native principles, utilizing micro-service architecture and containerization. This ensures that it can effectively run on platforms like ElegantRL-Podracer and FinRL-Podracer.
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Scalability: It fully harnesses the power of parallel computing, making it possible to deploy across vast numbers of computational nodes, such as in NVIDIA's DGX SuperPOD.
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Elasticity: ElegantRL offers dynamic allocation of computing resources, allowing it to adapt to the demands of the task at hand.
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Lightweight and Efficient: With a core codebase under 1,000 lines, ElegantRL is streamlined yet highly effective, outperforming alternatives like Ray RLlib in various test scenarios.
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Stability and Practicality: It is noted for its high stability over other frameworks like Stable Baselines 3 and is already employed in numerous projects, such as RLSolver and FinRL.
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Massive Parallel Simulations: Designed for high-speed sampling with GPU-based environments, making it suitable for complex simulations requiring large computational resources.
Supported Algorithms
ElegantRL supports a wide array of model-free DRL algorithms, including:
- For Continuous Actions: DDPG, TD3, SAC, PPO, REDQ.
- For Discrete Actions: DQN, Double DQN, D3QN.
- In Multi-agent Environments: QMIX, VDN, MADDPG, MAPPO, MATD3.
These algorithms cover both single-agent and multi-agent environments, offering flexibility in experiment designs.
Available Simulators
To cater to various simulation needs, ElegantRL provides support for:
- Isaac Gym: Enables large-scale simulations.
- OpenAI Gym, MuJoCo, PyBullet, FinRL: These are used for benchmarking DRL algorithms.
Getting Started: ElegantRL-Helloworld
For those new to the platform, the ElegantRL-Helloworld tutorial provides a comprehensive guide to executing initial experiments effectively. It offers a step-by-step approach to understanding and utilizing DRL algorithms like DQN, DDPG, and PPO through practical code examples and explanations.
Performance and Testing
- Efficiency: Illustrated by its superior performance over other libraries such as Ray RLlib and its remarkable processing speed in environments like Isaac Gym.
- Stability: Achieves lower variance and faster completion in tests compared to offerings like Stable Baseline3.
Testing in ElegantRL is made seamless with Python's unittest module, ensuring the stability and reliability of code contributions and updates.
Requirements
To run ElegantRL, users need Python 3.6+ and PyTorch 1.6+. Additional dependencies for optional features include libraries like Numpy, gym, pybullet, and matplotlib.
Contributions and Community
The ElegantRL project is open to contributions from the community. Developers are encouraged to maintain code quality, following rigorous testing standards before submitting changes.
In summary, ElegantRL stands out with its cloud-native architecture, vast scalability, and impressive efficiency, offering a robust platform for researchers and developers in the field of deep reinforcement learning.