Flappy-Bird-AI

by Code-Bullet JavaScript Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Flappy-Bird-AI Summary

Flappy-Bird-AI is a JavaScript library. Flappy-Bird-AI has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Flappy-Bird-AI

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Quality

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Support

Flappy-Bird-AI has a low active ecosystem.

It has 184 star(s) with 86 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 5 have been closed. On average issues are closed in 20 days. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Flappy-Bird-AI is current.

Quality

Flappy-Bird-AI has 0 bugs and 0 code smells.

Security

Flappy-Bird-AI has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Flappy-Bird-AI code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Flappy-Bird-AI is licensed under the GPL-3.0 License. This license is Strong Copyleft.

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

Reuse

Flappy-Bird-AI releases are not available. You will need to build from source code and install.

Top functions reviewed by kandi - BETA

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

Parse a CFF string
parse code from code
Converts a font - table to an icon font table .
Convert a path or path to curve
Parse the table buffer
Parse the glyph data
create name table
Body constructor .
Blur a canvas
Parse a vertex object

Get all kandi verified functions for this library.

Flappy-Bird-AI Key Features

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Flappy-Bird-AI Examples and Code Snippets

No Code Snippets are available at this moment for Flappy-Bird-AI.

Community Discussions

Trending Discussions on Flappy-Bird-AI

Training Neural Network to play flappy bird with genetic algorithm - Why can't it learn?

QUESTION

Training Neural Network to play flappy bird with genetic algorithm - Why can't it learn?

Asked 2017-Sep-15 at 11:19

I have been learning about neural networks and genetic algorithms, and to test my learning, have tried to make an AI that learns to play flappy bird:

I have left it running for at least 10 hours (overnight and longer), but the fittest member still fails to show any significant advancements in intelligence from when I began the simulation apart from avoiding the floor and ceilings. The inputs are the rays (as you can see above) that act as sight lines, and the network is fed in their lengths, and the birds vertical velocity. It seems that the best bird is essentially ignoring all the sight lines except the horizontal one, and when it is very short, it is jumping. The output is a number between 0 and 1, if the output is larger than 0.5, then the bird jumps. There are 4 hidden layers, with 15 neurons each, with the input layer feeding forward to the first hidden layers, then the 1st hidden layer feeding forward to the 2nd one ... and the final hidden layer feeding forward to the output, the dna of a bird is an array of real numbers representing the weights of the neural networks, I have made another project using the same style of neural network, and genetic algorithm, in which ants had to travel to food, and it worked perfectly.

Here is the code: https://github.com/Karan0110/flappy-bird-ai

Please say in the comments if you need any additional information

Please can you say whether my method is flawed or not, as I am almost certain the code works correctly (I got from the previous working project).

...

ANSWER

Answered 2017-Sep-15 at 11:19

I like your idea, but I suggest you change some things.

Don't use a network with a fixed structure. Look up Neural evolution of autgmenting topologies and rather implement it yourself, or use a library like neataptic.
- I don't believe your network needs that many inputs. I believe 3-5 sensors (20-50° gaps) would be enough, since many of the input values seem to be very similar.

If you are not sure why exactly your project is not working try this:

Try view an image of your current best network. If the network doesn't take important sensors (like the velocity) into account, you'll see it instantly.
Make sure all of your sensors are working fine (looks fine in the image above) and be sure to nkrmalize the values in a meaningful way.
Check if the maximum & average score increases over time. If it doesn't your GA isn't working properly or your networ receives inputs that are not good enough to solve the problem.

One trick that helped me out a lot, is to keep the elite of the GA in a seperate array. Only replace elite networks if some other network has performed better than the elite. Keep the elite trough all the generations, so once your algorithm finds an extraordinarily good solution, it won't be lost in any future generation if nothing else performs better.

Source https://stackoverflow.com/questions/45371437

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

Vulnerabilities

No vulnerabilities reported

Install Flappy-Bird-AI

You can download it from GitHub.

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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