torchRL VS snakeAI

Compare torchRL vs snakeAI and see what are their differences.

torchRL

TorchRL is a C++ reinforcement library using PyTorch C++ backend LibTorch (by adepierre)

snakeAI

testing MLP, DQN, PPO, SAC, policy-gradient by snake (by WorldEditor50)
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torchRL snakeAI
1 1
4 10
- -
0.0 5.5
almost 2 years ago 2 months ago
C++ C++
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.
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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.

torchRL

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

snakeAI

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

What are some alternatives?

When comparing torchRL and snakeAI you can also consider the following projects:

SegmentationCpp - A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: VGG, ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.

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

LibtorchTutorials - This is a code repository for pytorch c++ (or libtorch) tutorial.

recurrent-ppo-truncated-bptt - Baseline implementation of recurrent PPO using truncated BPTT

Drogon-torch-serve - Serve pytorch / torch models using Drogon

tianshou - An elegant PyTorch deep reinforcement learning library.

muzero-cpp - A C++ pytorch implementation of MuZero

open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

vowpal_wabbit - Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.