pytorch-symbolic VS gdrl

Compare pytorch-symbolic vs gdrl and see what are their differences.

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pytorch-symbolic gdrl
3 1
57 747
- -
4.7 10.0
about 1 year ago over 2 years ago
Jupyter Notebook Jupyter Notebook
MIT License BSD 3-clause "New" or "Revised" 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.

pytorch-symbolic

Posts with mentions or reviews of pytorch-symbolic. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-11-14.

gdrl

Posts with mentions or reviews of gdrl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-03.

What are some alternatives?

When comparing pytorch-symbolic and gdrl you can also consider the following projects:

torchview - torchview: visualize pytorch models

deep-rl-class - This repo contains the syllabus of the Hugging Face Deep Reinforcement Learning Course.

World4AI - World4AI is an educational resource for AI.

Andrew-NG-Notes - This is Andrew NG Coursera Handwritten Notes.

baselines - OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

Practical_RL - A course in reinforcement learning in the wild

Reinforcement-Learning - Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning

stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

conformal_classification - Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).

nn - 🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

machine-learning-book - Code Repository for Machine Learning with PyTorch and Scikit-Learn

ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.