deep-q-learning
rlalgorithms-tf2
| deep-q-learning | rlalgorithms-tf2 | |
|---|---|---|
| 1 | 18 | |
| 1,209 | 45 | |
| - | - | |
| 0.0 | 4.7 | |
| over 3 years ago | almost 2 years ago | |
| Python | Python | |
| MIT License | 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.
deep-q-learning
-
Deep Q Network knapsack problem
So go online on GitHub and find a DQN implementation that has options for using a feedforward net as input (instead of conv net as your input isn’t pixel based). Any remotely modular piece of code will take in state space size and action space as parameters to their NN. This is essentially setting input layer to be equal to state space (so 4) and output layer to be action space (201). (https://github.com/keon/deep-q-learning) this repo seems helpful i a cursory glance
rlalgorithms-tf2
-
I need suggestions to improve my project
Hello everyone, I published my python project a month ago, it's a command line interface for training, tuning and reusing reinforcement learning algorithms in tensorflow 2.x. It's similar to stable-baselines, tf-agents, and not so many others. It seems like it's not getting enough attention despite the README, license, and everything else.
-
My implementations of RL algorithms + demo and tutorial
Hello deep learners, I added a tutorial jupyter notebook, which walks you through the features quickly and easily. I posted here about my project earlier for those who haven't seen it before, it has my reusable implementations of reinforcement learning algorithms available from the command line. I also added the project to pypi, which makes it available through pip install xagents
-
RL command line tool demo + notebook
I created a command line tool for training and tuning and re-using reinforcement learning algorithms. For more info, you can check the project, and if you like you may also try the notebook I just added which walks you through how to use the features simply and quickly.
- Xagents: Deep reinforcement learning command line tool box
-
xagents: deep reinforcement learning command line tool box
Project page
-
Reinforcement learning quick start using OpenAI gym + xagents + Google Colab
xagents: python library based on tensorflow, which I developed, and it provides a command line interface for training and tuning algorithms on various environments.
- Xagents: Deep reinforcement learning Python library
- Autonomous learning command line tool box
- Elegant command line autonomous learning utility in Python
What are some alternatives?
Tetris-deep-Q-learning-pytorch - Deep Q-learning for playing tetris game
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
deep-RL-trading - playing idealized trading games with deep reinforcement learning
IRL - Algorithms for Inverse Reinforcement Learning
chainerrl - ChainerRL is a deep reinforcement learning library built on top of Chainer.
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
TensorLayer - Deep Learning and Reinforcement Learning Library for Scientists and Engineers
pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
tf2multiagentrl - Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x
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.
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.