Package Health Score

51 / 100

security
Security review needed
popularity
Small
maintenance
Inactive
community
Sustainable

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Popularity

Small

GitHub Stars: 1.19K
Forks: 157
Contributors: 20

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package pfrl, we found that it has been starred 1,188 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Jul 16, 2023
Direct Vulnerabilities: 0
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0
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0
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0
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Indirect Vulnerabilities: 0
C
0
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0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

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Maintenance

Inactive

Commit Frequency

Open Issues: 29
Open PR: 6
Last Release: 1 year ago
Last Commit: 4 months ago

Further analysis of the maintenance status of pfrl based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for pfrl is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Sustainable

Readme: Yes
Contributing.md: Yes
Code of Conduct: No
Contributors: 20
Funding: No

With more than 10 contributors for the pfrl repository, this is possibly a sign for a growing and inviting community.

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Package

Python Versions Compatibility: Unspecified

Age: 5 years
Latest Release: 1 year ago
Dependencies: N/A
Versions: 6
Maintainers: 1
Wheels: No

Readme

PFRL

PFRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using PyTorch.

Boxing Humanoid Grasping Atlas SlimeVolley

Installation

PFRL is tested with Python 3.7.7. For other requirements, see requirements.txt.

PFRL can be installed via PyPI:

pip install pfrl

It can also be installed from the source code:

python setup.py install

Refer to Installation for more information on installation.

Getting started

You can try PFRL Quickstart Guide first, or check the examples ready for Atari 2600 and Open AI Gym.

For more information, you can refer to PFRL's documentation.

Blog Posts

Algorithms

Algorithm	Discrete Action	Continous Action	Recurrent Model	Batch Training	CPU Async Training	Pretrained models^*
DQN (including DoubleDQN etc.)	✓	✓ (NAF)	✓	✓	x	✓
Categorical DQN	✓	x	✓	✓	x	x
Rainbow	✓	x	✓	✓	x	✓
IQN	✓	x	✓	✓	x	✓
DDPG	x	✓	x	✓	x	✓
A3C	✓	✓	✓	✓ (A2C)	✓	✓
ACER	✓	✓	✓	x	✓	x
PPO	✓	✓	✓	✓	x	✓
TRPO	✓	✓	✓	✓	x	✓
TD3	x	✓	x	✓	x	✓
SAC	x	✓	x	✓	x	✓

^*Note on Pretrained models: PFRL provides pretrained models (sometimes called a 'model zoo') for our reproducibility scripts on Atari environments (DQN, IQN, Rainbow, and A3C) and Mujoco environments (DDPG, TRPO, PPO, TD3, SAC), for each benchmarked environment.

Following algorithms have been implemented in PFRL:

A2C (Synchronous variant of A3C)
- examples: [atari (batched)]
A3C (Asynchronous Advantage Actor-Critic)
- examples: [atari reproduction] [atari]
ACER (Actor-Critic with Experience Replay)
- examples: [atari]
Categorical DQN
- examples: [atari] [general gym]
DQN (Deep Q-Network) (including Double DQN, Persistent Advantage Learning (PAL), Double PAL, Dynamic Policy Programming (DPP))
- examples: [atari reproduction] [atari] [atari (batched)] [flickering atari] [general gym]
DDPG (Deep Deterministic Policy Gradients) (including SVG(0))
- examples: [mujoco reproduction]
IQN (Implicit Quantile Networks)
- examples: [atari reproduction]
PPO (Proximal Policy Optimization)
- examples: [mujoco reproduction] [atari]
Rainbow
- examples: [atari reproduction] [Slime volleyball]
REINFORCE
- examples: [general gym]
SAC (Soft Actor-Critic)
- examples: [mujoco reproduction] [Atlas walk]
TRPO (Trust Region Policy Optimization) with GAE (Generalized Advantage Estimation)
- examples: [mujoco reproduction]
TD3 (Twin Delayed Deep Deterministic policy gradient algorithm)
- examples: [mujoco reproduction]

Following useful techniques have been also implemented in PFRL:

NoisyNet
- examples: [Rainbow] [DQN/DoubleDQN/PAL]
Prioritized Experience Replay
- examples: [Rainbow] [DQN/DoubleDQN/PAL]
Dueling Network
- examples: [Rainbow] [DQN/DoubleDQN/PAL]
Normalized Advantage Function
- examples: [DQN] (for continuous-action envs only)
Deep Recurrent Q-Network
- examples: [DQN]

Environments

Environments that support the subset of OpenAI Gym's interface (reset and step methods) can be used.

Contributing

Any kind of contribution to PFRL would be highly appreciated! If you are interested in contributing to PFRL, please read CONTRIBUTING.md.

License

MIT License.

Citations

To cite PFRL in publications, please cite our paper on ChainerRL, the library on which PFRL is based:

@article{JMLR:v22:20-376,
  author  = {Yasuhiro Fujita and Prabhat Nagarajan and Toshiki Kataoka and Takahiro Ishikawa},
  title   = {ChainerRL: A Deep Reinforcement Learning Library},
  journal = {Journal of Machine Learning Research},
  year    = {2021},
  volume  = {22},
  number  = {77},
  pages   = {1-14},
  url     = {http://jmlr.org/papers/v22/20-376.html}
}

FAQs

What is pfrl?

PFRL, a deep reinforcement learning library. Visit Snyk Advisor to see a full health score report for pfrl, including popularity, security, maintenance & community analysis.

Is pfrl popular?

The python package pfrl receives a total of 179 weekly downloads. As such, pfrl popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is pfrl well maintained?

We found indications that pfrl is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is pfrl safe to use?

While scanning the latest version of pfrl, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 25 November-2024, at 03:04 (UTC).

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pfrl

PFRL, a deep reinforcement learning library For more information about how to use this package see README

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Package

Readme

PFRL

Installation

Getting started

Blog Posts

Algorithms

Environments

Contributing

License

Citations

FAQs

What is pfrl?

Is pfrl popular?

Is pfrl well maintained?

Is pfrl safe to use?

Build a secure application checklist

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Scan your app for vulnerabilities

Fix identified vulnerabilities

Monitor for new issues

Package Health Score

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Direct Usage Popularity

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Commit Frequency

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Readme

PFRL

Installation

Getting started

Blog Posts

Algorithms

Environments

Contributing

License

Citations

FAQs

What is pfrl?

Is pfrl popular?

Is pfrl well maintained?

Is pfrl safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

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