Deep-Q-Learning
Tensorflow implementation of Deepminds dqn with double dueling networks (by fg91)
Deep-Reinforcement-Learning-Algorithms-with-PyTorch
PyTorch implementations of deep reinforcement learning algorithms and environments (by p-christ)
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Deep-Q-Learning | Deep-Reinforcement-Learning-Algorithms-with-PyTorch | |
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1 | 2 | |
206 | 5,416 | |
- | - | |
0.0 | 3.6 | |
almost 4 years ago | 8 months ago | |
Jupyter Notebook | Python | |
- | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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
Posts with mentions or reviews of Deep-Q-Learning.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-07.
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Frustrated beginner: How to approach/practice implementing papers into code?
When I started my master thesis last year I was a complete noob in ML, let alone RL. I tried to search for some code I could finally understand and stumbled upon this pretty nice notebook: https://github.com/fg91/Deep-Q-Learning It's by far the best notebook I've worked with yet. Since my goal was to learn PyTorch instead of Tensorflow (used in the notebook, it's also not working properly without tweaks due an old version of TF), I started re-implementing the code in PyTorch. Good thing is that you can compare your own results to the notebook and debug everything with prints if needed. That way I learned a lot about PyTorch and DQN.
Deep-Reinforcement-Learning-Algorithms-with-PyTorch
Posts with mentions or reviews of Deep-Reinforcement-Learning-Algorithms-with-PyTorch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-07.
-
Frustrated beginner: How to approach/practice implementing papers into code?
This series is actually easier than the David Silver one. Also here is the github repository link - https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
- Best PyTorch RL library for doing research
What are some alternatives?
When comparing Deep-Q-Learning and Deep-Reinforcement-Learning-Algorithms-with-PyTorch you can also consider the following projects:
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
tianshou - An elegant PyTorch deep reinforcement learning library.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
mtrl - Multi Task RL Baselines
mbrl-lib - Library for Model Based RL
sample-factory - High throughput synchronous and asynchronous reinforcement learning
rlpyt - Reinforcement Learning in PyTorch
Deep-Q-Learning vs stable-baselines3
Deep-Reinforcement-Learning-Algorithms-with-PyTorch vs stable-baselines3
Deep-Reinforcement-Learning-Algorithms-with-PyTorch vs tianshou
Deep-Reinforcement-Learning-Algorithms-with-PyTorch vs cleanrl
Deep-Reinforcement-Learning-Algorithms-with-PyTorch vs machin
Deep-Reinforcement-Learning-Algorithms-with-PyTorch vs mtrl
Deep-Reinforcement-Learning-Algorithms-with-PyTorch vs mbrl-lib
Deep-Reinforcement-Learning-Algorithms-with-PyTorch vs sample-factory
Deep-Reinforcement-Learning-Algorithms-with-PyTorch vs rlpyt