TensorLayer
DeepRL-TensorFlow2
| TensorLayer | DeepRL-TensorFlow2 | |
|---|---|---|
| 1 | 2 | |
| 7,275 | 573 | |
| 0.0% | - | |
| 0.0 | 0.0 | |
| about 1 year ago | almost 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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TensorLayer
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Introduction to ‘TensorLayer’: A Python-based Versatile Deep Learning Library Designed for Machine Learning Researchers
Github: https://github.com/tensorlayer/TensorLayer
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?
nngen - NNgen: A Fully-Customizable Hardware Synthesis Compiler for Deep Neural Network
soft-actor-critic - Re-implementation of Soft-Actor-Critic (SAC) in TensorFlow 2.0
chainerrl - ChainerRL is a deep reinforcement learning library built on top of Chainer.
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
gpt-j-chatbot - A GPT-J Chatbot Template for creating AI Characters (Virtual Girlfriend Chatbot, Replika-esque)
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
yagooglesearch - Yet another googlesearch - A Python library for executing intelligent, realistic-looking, and tunable Google searches.
ydata-synthetic - Synthetic data generators for tabular and time-series data
SRGAN - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
biprop - Identify a binary weight or binary weight and activation subnetwork within a randomly initialized network by only pruning and binarizing the network.
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...