rl_lib VS FinRL-Trading

Compare rl_lib vs FinRL-Trading and see what are their differences.

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rl_lib FinRL-Trading
2 1
27 1,880
- 1.8%
0.0 2.9
almost 3 years ago 3 months ago
Jupyter Notebook Jupyter Notebook
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

rl_lib

Posts with mentions or reviews of rl_lib. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-01-12.

FinRL-Trading

Posts with mentions or reviews of FinRL-Trading. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing rl_lib and FinRL-Trading you can also consider the following projects:

FinRL - FinRL: Financial Reinforcement Learning. 🔥

FinRL - Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance. NeurIPS 2020 & ICAIF 2021. 🔥 [Moved to: https://github.com/AI4Finance-Foundation/FinRL]

FinRL-Library - Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance. NeurIPS 2020 & ICAIF 2021. 🔥 [Moved to: https://github.com/AI4Finance-Foundation/FinRL]

Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020 - Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020. Please star. [Moved to: https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020]

garage - A toolkit for reproducible reinforcement learning research.

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.