ElegantRL
pomdp-baselines
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| ElegantRL | pomdp-baselines | |
|---|---|---|
| 6 | 5 | |
| 3,436 | 276 | |
| 3.2% | - | |
| 7.4 | 4.3 | |
| 8 days ago | 7 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ElegantRL
- Does “massively parallel simulation” help advance Reinforcement Learning?
- ElegantRL: Cloud-Native Deep Reinforcement Learning
- ElegantRL: A Lightweight and Stable Deep Reinforcement Learning Library
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[R] ElegantRL: A Lightweight and Stable Deep Reinforcement Learning Library
The ElegantRL library is featured with “elegant” in the following aspects:
- Lightweight, Efficient and Stable DRL Library
- Lightweight, Efficient and Stable DRL Implementation Using PyTorch
pomdp-baselines
- Best recurrent RL library?
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In Latest Machine Learning Research, A Group at CMU Release a Simple and Efficient Implementation of Recurrent Model-Free Reinforcement Learning (RL) for Future Work to Use as a Baseline for POMDP Algorithms
Continue reading| Check out the paper, github link, project and reference article.
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[R] Recurrent Model-Free RL is a Strong Baseline for Many POMDPs
Code for https://arxiv.org/abs/2110.05038 found: https://github.com/twni2016/pomdp-baselines
What are some alternatives?
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
tianshou - An elegant PyTorch deep reinforcement learning library.
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
Deep-Reinforcement-Learning-Algorithms - 32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.
recurrent-ppo-truncated-bptt - Baseline implementation of recurrent PPO using truncated BPTT
pytorch-ddpg - Deep deterministic policy gradient (DDPG) in PyTorch 🚀
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...