DPFRL | Discriminative Particle Filter Reinforcement Learning

by Yusufma03 Python Version: Current License: AGPL-3.0

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | DPFRL Summary

DPFRL is a Python library. DPFRL has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

The PyTorch implementation of DPFRL:. Xiao Ma, Peter Karkus, David Hsu, Wee Sun Lee, Nan Ye: Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations. International Conference on Learning Representations (ICLR), 2020.

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Quality

Security

License

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Support

DPFRL has a low active ecosystem.

It has 21 star(s) with 1 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DPFRL is current.

Quality

DPFRL has no bugs reported.

Security

DPFRL has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

DPFRL is licensed under the AGPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

DPFRL releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed DPFRL and discovered the below as its top functions. This is intended to give you an instant insight into DPFRL implemented functionality, and help decide if they suit your requirements.

Setup the model
Create an environment
Create a PFRNN model
Register and create environment variables
Render the scene
Gets X Y and Y
Compute reward
Bivariate Normal distribution
Log training and print results
Load the results from the monitor
Run a single model objective function
Remove noise from an observation
Generate a policy from current state
Encodes an observation
Reset the observation
Compute the mean and log standard deviation
Get environment yaml
Calculate the action of the function
Load training images
Compute returns
Saves model to directory
Given a policy return a dictionary of values
Calculates the rewards for each episode
Performs a single step
Forward computation
Detach the given state

Get all kandi verified functions for this library.

DPFRL Key Features

No Key Features are available at this moment for DPFRL.

DPFRL Examples and Code Snippets

No Code Snippets are available at this moment for DPFRL.

Community Discussions

No Community Discussions are available at this moment for DPFRL.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install DPFRL

You can choose either to use Docker or install dependencies yourself. I strongly recommend you to use Docker :).
To test on the Natural Flickering Atari games benchmark, please first download the data here, and put it at the root of your folder.

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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