tensorflow-rl-pong | Pong AI trained using policy | Machine Learning library

by mrahtz Python Version: Current License: No License

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kandi X-RAY | tensorflow-rl-pong Summary

tensorflow-rl-pong is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. tensorflow-rl-pong has no bugs, it has no vulnerabilities and it has low support. However tensorflow-rl-pong build file is not available. You can download it from GitHub.

Pong AI trained using policy gradient-based reinforcement learning

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tensorflow-rl-pong has a low active ecosystem.

It has 47 star(s) with 19 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 505 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-rl-pong is current.

Quality

tensorflow-rl-pong has 0 bugs and 0 code smells.

Security

tensorflow-rl-pong has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorflow-rl-pong code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorflow-rl-pong 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.

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tensorflow-rl-pong releases are not available. You will need to build from source code and install.

tensorflow-rl-pong has no build file. You will be need to create the build yourself to build the component from source.

tensorflow-rl-pong saves you 59 person hours of effort in developing the same functionality from scratch.

It has 154 lines of code, 7 functions and 2 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Initialize the agent .
Train the model .
Compute discounted rewards .
Preprog prepro .
Perform a forward pass on the model .
Loads checkpoint .

Get all kandi verified functions for this library.

tensorflow-rl-pong Key Features

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tensorflow-rl-pong Examples and Code Snippets

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Community Discussions

Trending Discussions on tensorflow-rl-pong

Negative reward in reinforcement learning

QUESTION

Negative reward in reinforcement learning

Asked 2019-Feb-19 at 12:43

I can't wrap my head around question: how exactly negative rewards helps machine to avoid them?

Origin of the question came from google's solution for game Pong. By their logic, once game finished (agent won or lost point), environment returns reward (+1 or -1). Any intermediate states return 0 as reward. That means each win/loose will return either [0,0,0,...,0,1] either [0,0,0,...,0,-1] reward arrays. Then they discount and standardize rewards:

...

ANSWER

Answered 2019-Feb-19 at 11:42

"Tensorflow optimizer minimize loss by absolute value (doesn't care about sign, perfect loss is always 0). Right?"

Wrong. Minimizing the loss means trying to achieve as small a value as possible. That is, -100 is "better" than 0. Accordingly, -7.2 is better than 7.2. Thus, a value of 0 really carries no special significance, besides the fact that many loss functions are set up such that 0 determines the "optimal" value. However, these loss functions are usually set up to be non-negative, so the question of positive vs. negative values doesn't arise. Examples are cross entropy, squared error etc.

Source https://stackoverflow.com/questions/54759181

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

Vulnerabilities

No vulnerabilities reported

Install tensorflow-rl-pong

You can download it from GitHub.
You can use tensorflow-rl-pong 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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