pytorch-A3C
Simple A3C implementation with pytorch + multiprocessing (by MorvanZhou)
tianshou
An elegant PyTorch deep reinforcement learning library. (by thu-ml)
pytorch-A3C | tianshou | |
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3 | 8 | |
568 | 7,459 | |
- | 2.0% | |
0.0 | 9.5 | |
about 1 year ago | 5 days ago | |
Python | Python | |
MIT License | 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.
pytorch-A3C
Posts with mentions or reviews of pytorch-A3C.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-11-17.
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Formula to compute loss in A3C
I'm a beginner to RL and I'm trying to understand how the loss function was computed. If it follows a specific formular. I've read the a3c algorithm overview on paper by barto but it seems the implemtation here https://github.com/MorvanZhou/pytorch-A3C/blob/master/discrete_A3C.py is different.
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How to measure the performance of a3c algorithm
I'm new to RL and i just started going through this implementation of a3c https://github.com/MorvanZhou/pytorch-A3C
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Tensorflow vs PyTorch for A3C
For the A3C part, I would appreciate your insights on whether to use Tensorflow or PyTorch to implement the algorithm. This GitHub https://github.com/MorvanZhou/pytorch-A3C tries to explain some things but it still isn't very clear to me which is the best, as I see that many implementations with TensorFlow. So if you have anything to add to help me choose one framework, I would very thankful.
tianshou
Posts with mentions or reviews of tianshou.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-02.
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Is it better to not use the Target Update Frequency in Double DQN or depends on the application?
The tianshou implementation I found at https://github.com/thu-ml/tianshou/blob/master/tianshou/policy/modelfree/dqn.py is DQN by default.
- 他們能回來嗎
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Multi-Agent Stable Baselines
https://github.com/thu-ml/tianshou Imho there isn't a library that has it all, RLlib is quite good too, but I think that Tianshou is more similar to Pytorch and that helps to change the internals more intuitively and know what you are doing.
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Question about the old policy and new policy in TRPO code
Good point...I'll check in more detail when I get a chance later today! I would suggest looking at a more recent implementation like https://github.com/DLR-RM/stable-baselines3 or https://github.com/thu-ml/tianshou if you're trying to build. https://spinningup.openai.com/en/latest/algorithms/trpo.html is particularly good for understanding
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Tensorflow vs PyTorch for A3C
Do you absolutely need A3C? A2C has become more widely used (see, e.g., the comment in https://github.com/ikostrikov/pytorch-a3c, and the fact that both https://github.com/thu-ml/tianshou and https://github.com/facebookresearch/salina have A2C implementations, but no A3C at first glance).
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"Tianshou: a Highly Modularized Deep Reinforcement Learning Library", Weng et al 2021 (Python PyTorch MuJuCo; PPO, DQN, A2C, DDPG, SAC, TD3, REINFORCE, NPG, TRPO, ACKTR)
Code for https://arxiv.org/abs/2107.14171 found: https://github.com/thu-ml/tianshou/
Get the code for Tianshou here (GitHub).
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Best PyTorch RL library for doing research
I tried tianshou and thought it was well-designed for modularity, but it was early in development when I tried and missing some basic features