RL-CabDriver | You are hired as a Sr
kandi X-RAY | RL-CabDriver Summary
kandi X-RAY | RL-CabDriver Summary
RL-CabDriver is a Jupyter Notebook library. RL-CabDriver has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.
You are hired as a Sr. Machine Learning Er. at SuperCabs, a leading app-based cab provider in a large Indian metro city. In this highly competitive industry, retention of good cab drivers is a crucial business driver, and you believe that a sound RL-based system for assisting cab drivers can potentially retain and attract new cab drivers. Cab drivers, like most people, are incentivised by a healthy growth in income. The goal of your project is to build an RL-based algorithm which can help cab drivers maximise their profits by improving their decision-making process on the field. There are some basic rules governing the ride-allocation system. If the cab is already in use, then the driver won’t get any requests. Otherwise, he may get multiple request(s). He can either decide to take any one of these requests or can go ‘offline’, i.e., not accept any request at all. In this project, you need to create the environment and an RL agent that learns to choose the best request. You need to train your agent using vanilla Deep Q-learning (DQN) only and NOT a double DQN.
You are hired as a Sr. Machine Learning Er. at SuperCabs, a leading app-based cab provider in a large Indian metro city. In this highly competitive industry, retention of good cab drivers is a crucial business driver, and you believe that a sound RL-based system for assisting cab drivers can potentially retain and attract new cab drivers. Cab drivers, like most people, are incentivised by a healthy growth in income. The goal of your project is to build an RL-based algorithm which can help cab drivers maximise their profits by improving their decision-making process on the field. There are some basic rules governing the ride-allocation system. If the cab is already in use, then the driver won’t get any requests. Otherwise, he may get multiple request(s). He can either decide to take any one of these requests or can go ‘offline’, i.e., not accept any request at all. In this project, you need to create the environment and an RL agent that learns to choose the best request. You need to train your agent using vanilla Deep Q-learning (DQN) only and NOT a double DQN.
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RL-CabDriver has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
RL-CabDriver has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of RL-CabDriver is current.
Quality
RL-CabDriver has no bugs reported.
Security
RL-CabDriver has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
RL-CabDriver 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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RL-CabDriver releases are not available. You will need to build from source code and install.
Installation instructions are not available. Examples and code snippets are available.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of RL-CabDriver
RL-CabDriver Key Features
No Key Features are available at this moment for RL-CabDriver.
RL-CabDriver Examples and Code Snippets
No Code Snippets are available at this moment for RL-CabDriver.
Community Discussions
No Community Discussions are available at this moment for RL-CabDriver.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install RL-CabDriver
You can download it from GitHub.
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