Deep-Reinforcement-Learning-Hands-On | Hands-on Deep Reinforcement Learning , published by Packt | Machine Learning library

by PacktPublishing Python Version: 01_release License: MIT

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

kandi X-RAY | Deep-Reinforcement-Learning-Hands-On Summary

Deep-Reinforcement-Learning-Hands-On is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. Deep-Reinforcement-Learning-Hands-On has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

This repository is being maintained by book author Max Lapan. I'm trying to keep all the examples working under the latest versions of PyTorch and gym, which is not always simple, as software evolves. For example, OpenAI Universe, extensively being used in chapter 13, was discontinued by OpenAI. List of current requirements is present in requirements.txt file. Examples require python 3.6. And, of course, bugs in examples are inevitable, so, exact code might differ from code present in the book text.

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Deep-Reinforcement-Learning-Hands-On has a medium active ecosystem.

It has 2583 star(s) with 1243 fork(s). There are 120 watchers for this library.

It had no major release in the last 6 months.

There are 25 open issues and 56 have been closed. On average issues are closed in 35 days. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Deep-Reinforcement-Learning-Hands-On is 01_release

Quality

Deep-Reinforcement-Learning-Hands-On has 0 bugs and 98 code smells.

Security

Deep-Reinforcement-Learning-Hands-On has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Deep-Reinforcement-Learning-Hands-On code analysis shows 0 unresolved vulnerabilities.

There are 18 security hotspots that need review.

License

Deep-Reinforcement-Learning-Hands-On is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Deep-Reinforcement-Learning-Hands-On 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.

Deep-Reinforcement-Learning-Hands-On saves you 5235 person hours of effort in developing the same functionality from scratch.

It has 10995 lines of code, 574 functions and 144 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Play a game
Perform a search
Returns the policy value for a given state
Finds the leaf node
Iterate over the examples in the given network
Compute discounted rewards based on the given rewards
Compute the difference between two observations
Clears the histogram
Workhorse function
Compute the gradient of the loss function
Calculate the advantage of the optimization
Encode the price as a numpy array
Perform one step
Play a single step in the environment
Calculates the reward
Move a state to a player
Benchmark a buffer
Clears the index
Train A2C model
Calculate a reward
Calculates the gradients for a given process
Perform validation
Handle text message
Load a demo
Compute the distribution of the distance between two atoms
Performs a trpo step
Calculate the loss for a given batch

Get all kandi verified functions for this library.

Deep-Reinforcement-Learning-Hands-On Key Features

No Key Features are available at this moment for Deep-Reinforcement-Learning-Hands-On.

Deep-Reinforcement-Learning-Hands-On Examples and Code Snippets

No Code Snippets are available at this moment for Deep-Reinforcement-Learning-Hands-On.

Community Discussions

Trending Discussions on Deep-Reinforcement-Learning-Hands-On

What does the notation self(x) do?

QUESTION

What does the notation self(x) do?

Asked 2020-Jan-09 at 16:45

I'm learning about Distributional RL from 'Deep Reinforcement Learning Hands On' code. And there is a method in model class:

...

ANSWER

Answered 2020-Jan-09 at 16:45

It will call the __call__ method on the instance. See this demo:

Source https://stackoverflow.com/questions/59668333

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

Vulnerabilities

No vulnerabilities reported

Install Deep-Reinforcement-Learning-Hands-On

You can download it from GitHub.
You can use Deep-Reinforcement-Learning-Hands-On 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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