asv
tianshou
| asv | tianshou | |
|---|---|---|
| 3 | 8 | |
| 840 | 7,406 | |
| 1.1% | 1.3% | |
| 9.1 | 9.5 | |
| 9 days ago | 6 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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.
asv
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git-appraise – Distributed Code Review for Git
> All these workflows are a derivation of the source in the repository and keeping them close together has a great aesthetic.
I agree. Version control is a great enabler, so using it to track "sources" other than just code can be useful. A couple of tools I like to use:
- Artemis, for tracking issues http://www.chriswarbo.net/blog/2017-06-14-artemis.html
- ASV, for tracking benchmark results https://github.com/airspeed-velocity/asv (I use this for non-Python projects via my asv-nix plugin http://www.chriswarbo.net/projects/nixos/asv_benchmarking.ht... )
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Is GitHub Actions suitable for running benchmarks?
scikit-image, the project that commissioned this task, uses Airspeed Velocity, or asv, for their benchmark tests.
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Memory benchmarking tools
Problem - The project currently uses Airspeed Velocity for tracking the memory changes. But I am having a lot of trouble setting this up and using this tool for monitoring memory consumption on a regular basis. Are you guys aware of some other open-source tools that I can use instead of this? I am stuck with this thing for some time now. I would appreciate any help.
tianshou
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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
What are some alternatives?
pyperformance - Python Performance Benchmark Suite
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
pybench - Python benchmark tool inspired by Geekbench.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
scikit-image - Image processing in Python
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:
pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
pyeventbus - Python Eventbus
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
pytest-benchmark - py.test fixture for benchmarking code
pytorch-a3c - PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".