TensorLayer VS rlalgorithms-tf2

Compare TensorLayer vs rlalgorithms-tf2 and see what are their differences.

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TensorLayer rlalgorithms-tf2
1 18
7,275 45
0.0% -
0.0 4.7
about 1 year ago almost 2 years ago
Python Python
GNU General Public License v3.0 or later 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.

TensorLayer

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

rlalgorithms-tf2

Posts with mentions or reviews of rlalgorithms-tf2. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-06.

What are some alternatives?

When comparing TensorLayer and rlalgorithms-tf2 you can also consider the following projects:

nngen - NNgen: A Fully-Customizable Hardware Synthesis Compiler for Deep Neural Network

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

chainerrl - ChainerRL is a deep reinforcement learning library built on top of Chainer.

IRL - Algorithms for Inverse Reinforcement Learning

gpt-j-chatbot - A GPT-J Chatbot Template for creating AI Characters (Virtual Girlfriend Chatbot, Replika-esque)

DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2

yagooglesearch - Yet another googlesearch - A Python library for executing intelligent, realistic-looking, and tunable Google searches.

tf2multiagentrl - Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x

SRGAN - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

DRL-robot-navigation - Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

biprop - Identify a binary weight or binary weight and activation subnetwork within a randomly initialized network by only pruning and binarizing the network.

agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.