DRLND_Continuous_Control | For this project , we will work with the Reacher environment

by voice32 Jupyter Notebook Version: Current License: No License

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kandi X-RAY | DRLND_Continuous_Control Summary

DRLND_Continuous_Control is a Jupyter Notebook library. DRLND_Continuous_Control has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

For this project, we will work with the Reacher environment. In this environment, a double-jointed arm can move to target locations. A reward of +0.1 is provided for each step that the agent's hand is in the goal location. Thus, the goal of the agent is to maintain its position at the target location for as many time steps as possible. The observation space consists of 33 variables corresponding to position, rotation, velocity, and angular velocities of the arm. Each action is a vector with four numbers, corresponding to torque applicable to two joints. Every entry in the action vector should be a number between -1 and 1.

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Quality

Security

License

Reuse

Support

DRLND_Continuous_Control 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.

DRLND_Continuous_Control has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DRLND_Continuous_Control is current.

Quality

DRLND_Continuous_Control has no bugs reported.

Security

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

License

DRLND_Continuous_Control 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

DRLND_Continuous_Control releases are not available. You will need to build from source code and install.

Installation instructions are available. Examples and code snippets are not available.

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DRLND_Continuous_Control Key Features

No Key Features are available at this moment for DRLND_Continuous_Control.

DRLND_Continuous_Control Examples and Code Snippets

No Code Snippets are available at this moment for DRLND_Continuous_Control.

Community Discussions

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

Vulnerabilities

No vulnerabilities reported

Install DRLND_Continuous_Control

Download the environment from one of the links below. You need only select the environment that matches your operating system:. (For Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system. (For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link (version 1) or this link (version 2) to obtain the "headless" version of the environment. You will not be able to watch the agent without enabling a virtual screen, but you will be able to train the agent. (To watch the agent, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.). Place the file in the DRLND GitHub repository, in the p2_continuous-control/ folder, and unzip (or decompress) the file.
Download the environment from one of the links below. You need only select the environment that matches your operating system: Version 1: One (1) Agent Linux: click here Mac OSX: click here Windows (32-bit): click here Windows (64-bit): click here Version 2: Twenty (20) Agents Linux: click here Mac OSX: click here Windows (32-bit): click here Windows (64-bit): click here (For Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system. (For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link (version 1) or this link (version 2) to obtain the "headless" version of the environment. You will not be able to watch the agent without enabling a virtual screen, but you will be able to train the agent. (To watch the agent, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.)
Place the file in the DRLND GitHub repository, in the p2_continuous-control/ folder, and unzip (or decompress) the file.

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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