TensorFlow2.0-for-Deep-Reinforcement-Learning
TensorFlow 2.0 for Deep Reinforcement Learning. :octopus: (by Huixxi)
DeepRL-TensorFlow2
🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2 (by marload)
| TensorFlow2.0-for-Deep-Reinforcement-Learning | DeepRL-TensorFlow2 | |
|---|---|---|
| 1 | 2 | |
| 81 | 573 | |
| - | - | |
| 0.0 | 0.0 | |
| 8 months ago | almost 2 years ago | |
| Python | Python | |
| - | Apache License 2.0 |
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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.
TensorFlow2.0-for-Deep-Reinforcement-Learning
Posts with mentions or reviews of TensorFlow2.0-for-Deep-Reinforcement-Learning.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-01-15.
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Beginner attempting to implement Noisy DQN
I forgot to say that I'm using tensorflow, nevertheless I managed to find a git implementation for tensorflow 2 of the noisy dense layer (https://github.com/Huixxi/TensorFlow2.0-for-Deep-Reinforcement-Learning/blob/master/07_noisynet.py) and tried to adapt it to my needs.
DeepRL-TensorFlow2
Posts with mentions or reviews of DeepRL-TensorFlow2.
We have used some of these posts to build our list of alternatives
and similar projects.
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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?
When comparing TensorFlow2.0-for-Deep-Reinforcement-Learning and DeepRL-TensorFlow2 you can also consider the following projects:
pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
soft-actor-critic - Re-implementation of Soft-Actor-Critic (SAC) in TensorFlow 2.0