Self_Driving_Car | A demonstration of Reinforcement Learning | Machine Learning library
kandi X-RAY | Self_Driving_Car Summary
kandi X-RAY | Self_Driving_Car Summary
Self_Driving_Car is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. Self_Driving_Car has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
This project was made while learning about Deep Q Learning which is a widely used technique for Reinforcement Learning. The idea behind this is simple, the car has 3 sensors which measure the density of the sand and also the edges of the environment. This input is then sent to a simple feed-forward Neural Network which takes the decision in which direction the car must move.
This project was made while learning about Deep Q Learning which is a widely used technique for Reinforcement Learning. The idea behind this is simple, the car has 3 sensors which measure the density of the sand and also the edges of the environment. This input is then sent to a simple feed-forward Neural Network which takes the decision in which direction the car must move.
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Self_Driving_Car has a low active ecosystem.
It has 6 star(s) with 2 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
Self_Driving_Car has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Self_Driving_Car is current.
Quality
Self_Driving_Car has 0 bugs and 0 code smells.
Security
Self_Driving_Car has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Self_Driving_Car code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Self_Driving_Car 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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Self_Driving_Car 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.
It has 268 lines of code, 22 functions and 2 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Self_Driving_Car and discovered the below as its top functions. This is intended to give you an instant insight into Self_Driving_Car implemented functionality, and help decide if they suit your requirements.
- Update the map
- Move the car
- Update the state
- Train the model
- Select a single action
- Push event to memory
- Generate random variables
- Calculate the reward score
- Build the game
- Start the car
- Save the plot
- Save the model to disk
- Load the last saved brain
- Loads the checkpoint
Get all kandi verified functions for this library.
Self_Driving_Car Key Features
No Key Features are available at this moment for Self_Driving_Car.
Self_Driving_Car Examples and Code Snippets
No Code Snippets are available at this moment for Self_Driving_Car.
Community Discussions
Trending Discussions on Self_Driving_Car
QUESTION
Error :_pickle.PicklingError: Can't pickle at 0x0000002F2175B048>: attribute lookup on __main__ failed
Asked 2020-Oct-14 at 05:55
I am trying to run following code that reported running well with other users, but I found this error.
-- coding: utf-8 -- Import the Stuff ...ANSWER
Answered 2020-Oct-14 at 05:55pickle doesn't pickle function objects. It expects to find the function object by importing its module and looking up its name. lambdas are anonymous functions (no name) so that doesn't work. The solution is to name the function at module level. The only lambda I found in your code is
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Self_Driving_Car
You can download it from GitHub.
You can use Self_Driving_Car 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.
You can use Self_Driving_Car 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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