tf_rl | Refinforcement learning framework

by dickreuter Python Version: Current License: No License

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kandi X-RAY | tf_rl Summary

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

Refinforcement learning framework

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tf_rl has a low active ecosystem.

It has 7 star(s) with 2 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

tf_rl has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tf_rl is current.

Quality

tf_rl has no bugs reported.

Security

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

License

tf_rl does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

tf_rl 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.

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tf_rl Key Features

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tf_rl Examples and Code Snippets

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Community Discussions

Trending Discussions on tf_rl

DQN algorithm does not converge on CartPole-v0

QUESTION

DQN algorithm does not converge on CartPole-v0

Asked 2019-Apr-06 at 19:56

Short Description of my model

I am trying to write my own DQN algorithm in Python, using Tensorflow following the paper(Mnih et al., 2015). In train_DQN function, I have defined the training procedure, and DQN_CartPole is for defining the function approximation(simple 3-layered Neural Network). For loss function, Huber loss or MSE is implemented followed by the gradient clipping(between -1 and 1). Then, I have implemented soft-update method instead of hard-update of the target network by copying the weights in the main network.

Question

I am trying it on the CartPole environment(OpenAI gym), but the rewards does not improve as it does in other people's algorithms, such as keras-rl. Any help will be appreciated.

reward over timestep

If possible, could you have a look at the source code?

...

ANSWER

Answered 2019-Apr-06 at 19:33

Briefly looking over, it seems that the dones variable is a binary vector where 1 denotes done, and 0 denotes not-done.

You then use dones here:

Source https://stackoverflow.com/questions/55552366

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

Vulnerabilities

No vulnerabilities reported

Install tf_rl

You can download it from GitHub.
You can use tf_rl like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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