DRLib | concise deep reinforcement | Reinforcement Learning library
kandi X-RAY | DRLib Summary
kandi X-RAY | DRLib Summary
DRLib is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Deep Learning, Pytorch applications. DRLib has no vulnerabilities, it has a Permissive License and it has low support. However DRLib has 20 bugs and it build file is not available. You can download it from GitHub.
DRLib：A concise deep reinforcement learning library, integrating HER and PER for almost off policy RL algos.
DRLib：A concise deep reinforcement learning library, integrating HER and PER for almost off policy RL algos.
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Quality
Security
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DRLib has a low active ecosystem.
It has 386 star(s) with 59 fork(s). There are 6 watchers for this library.
It had no major release in the last 6 months.
There are 2 open issues and 9 have been closed. On average issues are closed in 33 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of DRLib is current.
Quality
DRLib has 20 bugs (2 blocker, 0 critical, 14 major, 4 minor) and 465 code smells.
Security
DRLib has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
DRLib code analysis shows 0 unresolved vulnerabilities.
There are 1 security hotspots that need review.
License
DRLib is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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DRLib releases are not available. You will need to build from source code and install.
DRLib has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions, examples and code snippets are available.
It has 18163 lines of code, 1114 functions and 237 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed DRLib and discovered the below as its top functions. This is intended to give you an instant insight into DRLib implemented functionality, and help decide if they suit your requirements.
- Samples an environment
- Run an experiment
- Colorize a string
- Runs the experiment
- Print an announcement
- Train the model
- Compute the loss for the given objective function
- Compute the loss of the model
- Saves an episode to the norm
- Returns a list of all the agents in the sampler
- Evaluate the agent
- Apply a gaussian policy
- Saves the given episode transition and rewards
- Compute the mean reward of an agent
- Returns the mean reward of the agent
- Print function to stdout
- Calculate action
- Return a list of variants for the experiment
- Perform an mlp action critic
- Runs a test environment
- A mixture of examples
- Call an experiment
- Get command line arguments
- Function to plot success rate
- Launch the environment
- Make matplotlib plots
- Save an episode to the norm
Get all kandi verified functions for this library.
DRLib Key Features
No Key Features are available at this moment for DRLib.
DRLib Examples and Code Snippets
No Code Snippets are available at this moment for DRLib.
Community Discussions
Trending Discussions on DRLib
QUESTION
Sequential Colour Palettes Across Facets in ggplot2
Asked 2022-Jan-14 at 19:56
I'm building a dashboard which includes a lollypop graph of top 10 locations by number of visitors, faceted by year. Here is a roughly similar plot I constructed using dummy data:
To reorder the locations by total number of visitors per facet, I used reorder_within() and scale_x_reorder(), created by Tyler Rinker. For the sequential colour palette, I used RColorBrewer and scale_color_distiller.
I have three things I'd like to change about colors of the bars, and I'm unsure how to do any of them.
- I would like the colors to start from a bit darker rather than nearly white, because they're a bit hard to see.
- I would like each bar to have its own color, even when the visitor count is the same, and to have those colours be clearly distinguishable from each other, while still being sequential.
- I would like each facet to have the same colour scheme, so it looks consistent - I understand that the scheme differs across the facets because they all have different visitor numbers.
Reproducible example below with some dummy data:
...ANSWER
Answered 2022-Jan-14 at 19:56I'd suggest coloring by rank or scaled value within year. Below are two possibilities:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install DRLib
Clone the repo and cd into it:.
Clone the repo and cd into it: git clone https://github.com/kaixindelele/DRLib.git cd DRLib
Create anaconda DRLib_env env: conda create -n DRLib_env python=3.6.9 source activate DRLib_env
Install pip_requirement.txt: pip install -r pip_requirement.txt If installation of mpi4py fails, try the following command(Only this one can be installed successfully!): conda install mpi4py
Install tensorflow-gpu=1.14.0 conda install tensorflow-gpu==1.14.0 # if you have a CUDA-compatible gpu and proper drivers
Install torch torchvision # CUDA 9.2 conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=9.2 -c pytorch # CUDA 10.1 conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch # CUDA 10.2 conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch # CPU Only conda install pytorch==1.6.0 torchvision==0.7.0 cpuonly -c pytorch # or pip install pip --default-timeout=100 install torch -i http://pypi.douban.com/simple --trusted-host pypi.douban.com [pip install torch 在线安装!非离线!](https://blog.csdn.net/hehedadaq/article/details/111480313)
Install mujoco and mujoco-py refer to: https://blog.csdn.net/hehedadaq/article/details/109012048
Install gym[all] refer to https://blog.csdn.net/hehedadaq/article/details/110423154
Clone the repo and cd into it: git clone https://github.com/kaixindelele/DRLib.git cd DRLib
Create anaconda DRLib_env env: conda create -n DRLib_env python=3.6.9 source activate DRLib_env
Install pip_requirement.txt: pip install -r pip_requirement.txt If installation of mpi4py fails, try the following command(Only this one can be installed successfully!): conda install mpi4py
Install tensorflow-gpu=1.14.0 conda install tensorflow-gpu==1.14.0 # if you have a CUDA-compatible gpu and proper drivers
Install torch torchvision # CUDA 9.2 conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=9.2 -c pytorch # CUDA 10.1 conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch # CUDA 10.2 conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch # CPU Only conda install pytorch==1.6.0 torchvision==0.7.0 cpuonly -c pytorch # or pip install pip --default-timeout=100 install torch -i http://pypi.douban.com/simple --trusted-host pypi.douban.com [pip install torch 在线安装!非离线!](https://blog.csdn.net/hehedadaq/article/details/111480313)
Install mujoco and mujoco-py refer to: https://blog.csdn.net/hehedadaq/article/details/109012048
Install gym[all] refer to https://blog.csdn.net/hehedadaq/article/details/110423154
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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