Deep-Reinforcement-Learning-Algorithms-with-PyTorch VS Deep-Q-Learning

Compare Deep-Reinforcement-Learning-Algorithms-with-PyTorch vs Deep-Q-Learning and see what are their differences.

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

Tensorflow implementation of Deepminds dqn with double dueling networks (by fg91)
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Deep-Reinforcement-Learning-Algorithms-with-PyTorch Deep-Q-Learning
2 1
5,423 206
- -
3.6 0.0
9 months ago almost 4 years ago
Python Jupyter Notebook
MIT License -
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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.

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.
  • Frustrated beginner: How to approach/practice implementing papers into code?
    3 projects | /r/reinforcementlearning | 7 May 2021
    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.

What are some alternatives?

When comparing Deep-Reinforcement-Learning-Algorithms-with-PyTorch and Deep-Q-Learning 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-Reinforcement-Learning-Algorithms-with-PyTorch vs stable-baselines3 Deep-Q-Learning 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