Deep-Reinforcement-Learning-Algorithms
32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log. (by Rafael1s)
DeepRL-TensorFlow2
🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2 (by archsyscall)
| Deep-Reinforcement-Learning-Algorithms | DeepRL-TensorFlow2 | |
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| 3 | 2 | |
| 903 | 607 | |
| 0.0% | 0.0% | |
| 3.6 | 0.0 | |
| about 4 years ago | about 3 years ago | |
| Jupyter Notebook | Python | |
| - | Apache License 2.0 |
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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.
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-Reinforcement-Learning-Algorithms
Posts with mentions or reviews of Deep-Reinforcement-Learning-Algorithms.
We have used some of these posts to build our list of alternatives
and similar projects.
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Is there a canonical simple "helloworld" neural network design? Something beyond AND/OR logic, a handful of nodes that does something mildly "useful"?
I guess the most spectacular in terms of performance/"brain size" ratio is a 2 neuron, 8 weights network https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/CartPole-Policy-Based-Hill-Climbing
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Training time of CartPole is way to long
It can be solved in 113 episodes by Hill Climbing algorithm, https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/CartPole-Policy-Based-Hill-Climbingor by Double Deep Q-Learning in 612 episodes, https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/Cartpole-Double-Deep-Q-Learning
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Need help with PyTorch script for Actor_Critic implementation of MountainCar env.
You can find the solution for MountainCar env here: https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/MountainCarContinuous-TD3This solution implemented using PyTorch. The TD3 model is the successor to DDPG algorithm using the Actor-Critic model.
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 Deep-Reinforcement-Learning-Algorithms and DeepRL-TensorFlow2 you can also consider the following projects:
Popular-RL-Algorithms - PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
Reinforcement-Learning - Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning
Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020 - Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020. Please star. [Moved to: https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020]
ydata-synthetic - Synthetic data generators for tabular and time-series data
rl_lib - Series of deep reinforcement learning algorithms 🤖
IRL - Algorithms for Inverse Reinforcement Learning
Deep-Reinforcement-Learning-Algorithms vs Popular-RL-Algorithms
DeepRL-TensorFlow2 vs Reinforcement-Learning
Deep-Reinforcement-Learning-Algorithms vs Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020
DeepRL-TensorFlow2 vs ydata-synthetic
Deep-Reinforcement-Learning-Algorithms vs rl_lib
DeepRL-TensorFlow2 vs IRL