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

 by   PacktPublishing Python Version: 01_release License: MIT

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

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.

            kandi-support Support

              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

            kandi-Quality Quality

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

            kandi-Security 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.

            kandi-License 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.

            kandi-Reuse 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?
            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:



            Answered 2020-Jan-09 at 16:45

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


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


            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.


            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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