rlalgorithms-tf2
DeepRL-TensorFlow2
| rlalgorithms-tf2 | DeepRL-TensorFlow2 | |
|---|---|---|
| 18 | 2 | |
| 45 | 573 | |
| - | - | |
| 4.7 | 0.0 | |
| almost 2 years ago | almost 2 years ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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rlalgorithms-tf2
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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.
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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
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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
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xagents: deep reinforcement learning command line tool box
Project page
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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
DeepRL-TensorFlow2
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PPO implementation in TensorFlow2
I've been searching for a clean, good, and understandable implementation of PPO for continuous action space with TF2 witch is understandable enough for me to apply my modifications, but the closest thing that I have found is this code which seems to not work properly even on a simple gym cartpole env (discussed issues in git-hub repo suggest the same problem) so I have some doubts :). I was wondering whether you could recommend an implementation that you trust and suggest :)
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Question about using tf.stop_gradient in separate Actor-Critic networks for A2C implementation for TF2
I have been looking at this implementation of A2C. Here the author of the code uses stop_gradient only on the critic network at L90 bur not in the actor network L61 for the continuous case. However , it is used both in actor and critic networks for the discrete case. Can someone explain me why?
What are some alternatives?
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
soft-actor-critic - Re-implementation of Soft-Actor-Critic (SAC) in TensorFlow 2.0
IRL - Algorithms for Inverse Reinforcement Learning
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
TensorLayer - Deep Learning and Reinforcement Learning Library for Scientists and Engineers
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
tf2multiagentrl - Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x
ydata-synthetic - Synthetic data generators for tabular and time-series data
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.
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...