chainerrl | ChainerRL is a deep reinforcement | Machine Learning library
kandi X-RAY | chainerrl Summary
kandi X-RAY | chainerrl Summary
chainerrl is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. chainerrl has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.
ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL.
ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL.
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chainerrl has a medium active ecosystem.
It has 1085 star(s) with 230 fork(s). There are 74 watchers for this library.
It had no major release in the last 12 months.
There are 51 open issues and 147 have been closed. On average issues are closed in 370 days. There are 14 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of chainerrl is v0.8.0
Quality
chainerrl has 0 bugs and 0 code smells.
Security
chainerrl has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
chainerrl code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
chainerrl 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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chainerrl releases are available to install and integrate.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
chainerrl saves you 12721 person hours of effort in developing the same functionality from scratch.
It has 25609 lines of code, 1836 functions and 250 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed chainerrl and discovered the below as its top functions. This is intended to give you an instant insight into chainerrl implemented functionality, and help decide if they suit your requirements.
- Prepare the output directory
- Calculate and train the reward function
- Compute the objective function of the model
- Update rewards from the replay buffer
- Updates the optimizer
- Activate and train the reward function
- Calculate rewards from the replay buffer
- Compute the Poisson loss function
- Concatenate a list of states
- Performs the action and training the replay buffer
- Batches the given observation and rewards
- Batch and train the model
- Compute target values
- Actual act on the given obs and reward
- Calculate act and train the model
- Compute the Y and T
- Update the model from a list of episodes
- Batch observation and train the target network
- Perform a step
- Performs action and train the target network
- Calculate the loss function
- Performs batch act and training
- Perform the action and update the reward
- Performs batch action and training
- Starts training and training a reward
- Stops the evaluation of the given reward
Get all kandi verified functions for this library.
chainerrl Key Features
No Key Features are available at this moment for chainerrl.
chainerrl Examples and Code Snippets
No Code Snippets are available at this moment for chainerrl.
Community Discussions
Trending Discussions on chainerrl
QUESTION
PackagesNotFoundError: The following packages are not available from current channels, AFTER adding conda-forge channel?
Asked 2020-Dec-01 at 14:00
Even after adding conda forge channel as suggested here:
PackagesNotFoundError: The following packages are not available from current channels:
Conda cannot still install many of the packages in a requirements.txt file :
...ANSWER
Answered 2020-Nov-30 at 13:19To install PyTorch just try this Command in a shell
QUESTION
How to extend an agent class in ChainerRL in Python
Asked 2020-Nov-25 at 11:52
I want to extend the PPO agent class in ChainerRL. I did the following:
...ANSWER
Answered 2020-Nov-25 at 11:50action = super().act_and_train(obs, reward)
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install chainerrl
ChainerRL is tested with 3.6. For other requirements, see requirements.txt.
You can try ChainerRL Quickstart Guide first, or check the examples ready for Atari 2600 and Open AI Gym. For more information, you can refer to ChainerRL's documentation.
You can try ChainerRL Quickstart Guide first, or check the examples ready for Atari 2600 and Open AI Gym. For more information, you can refer to ChainerRL's documentation.
Support
Any kind of contribution to ChainerRL would be highly appreciated! If you are interested in contributing to ChainerRL, please read CONTRIBUTING.md.
Find more information at:
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