deeprl_p3_tennis | For this project , you will work with the Tennis environment

by revelationnow Jupyter Notebook Version: Current License: No License

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kandi X-RAY | deeprl_p3_tennis Summary

deeprl_p3_tennis is a Jupyter Notebook library. deeprl_p3_tennis has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

For this project, you will work with the Tennis environment. In this environment, two agents control rackets to bounce a ball over a net. If an agent hits the ball over the net, it receives a reward of +0.1. If an agent lets a ball hit the ground or hits the ball out of bounds, it receives a reward of -0.01. Thus, the goal of each agent is to keep the ball in play. The observation space consists of 8 variables corresponding to the position and velocity of the ball and racket. Each agent receives its own, local observation. Two continuous actions are available, corresponding to movement toward (or away from) the net, and jumping. The task is episodic, and in order to solve the environment, your agents must get an average score of +0.5 (over 100 consecutive episodes, after taking the maximum over both agents). Specifically,. The environment is considered solved, when the average (over 100 episodes) of those scores is at least +0.5.

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Support

deeprl_p3_tennis has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

deeprl_p3_tennis has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of deeprl_p3_tennis is current.

Quality

deeprl_p3_tennis has no bugs reported.

Security

deeprl_p3_tennis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

deeprl_p3_tennis 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

deeprl_p3_tennis 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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deeprl_p3_tennis Key Features

No Key Features are available at this moment for deeprl_p3_tennis.

deeprl_p3_tennis Examples and Code Snippets

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Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install deeprl_p3_tennis

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 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 p3_collab-compet/ 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: 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 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 p3_collab-compet/ 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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