spinningup | educational resource to help anyone learn deep | Reinforcement Learning library

 by   openai Python Version: 0.2 License: MIT

kandi X-RAY | spinningup Summary

kandi X-RAY | spinningup Summary

spinningup is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Reinforcement Learning applications. spinningup 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.

Status: Maintenance (expect bug fixes and minor updates).

            kandi-support Support

              spinningup has a medium active ecosystem.
              It has 8539 star(s) with 1987 fork(s). There are 224 watchers for this library.
              It had no major release in the last 12 months.
              There are 148 open issues and 118 have been closed. On average issues are closed in 24 days. There are 56 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of spinningup is 0.2

            kandi-Quality Quality

              spinningup has 0 bugs and 0 code smells.

            kandi-Security Security

              spinningup has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              spinningup code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              spinningup 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

              spinningup releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              spinningup saves you 2258 person hours of effort in developing the same functionality from scratch.
              It has 4937 lines of code, 366 functions and 71 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed spinningup and discovered the below as its top functions. This is intended to give you an instant insight into spinningup implemented functionality, and help decide if they suit your requirements.
            • Demo D3 test
            • Store observation data
            • Sample from the pool
            • Log a table of metrics
            • Configure log arguments for logging
            • Wrapper function for trpo
            • Compute the end of the path
            • Syncs the params
            • Assign parameters from a flat array
            • Mean gaussian policy
            • Gaussian likelihood function
            • Wrapper for vpg
            • Calculate the end of a path
            • Wrapper for ppo
            • Compute the gradient for a path
            • Runs an environment on a given environment
            • Multi - layer ML policy
            • Get statistics for a given epoch
            • Print the result
            • Compute the DiagonalGaussian Distribution
            • Returns a test set of variants
            • Compute the probability distribution for given observations
            • Make plot of data
            • Gaussian likelihood
            • Implementation of mlp_actor_critic
            • Simulate an environment
            • Train MNIST
            • Setup logging keyword arguments
            • Train a model
            • Parse and execute a grid search command
            Get all kandi verified functions for this library.

            spinningup Key Features

            No Key Features are available at this moment for spinningup.

            spinningup Examples and Code Snippets

            Pythondot img1Lines of Code : 32dot img1License : Strong Copyleft (GPL-3.0)
            copy iconCopy
            # Download the container (CPU version)
            docker pull vkurenkov/cognitive-robotics:cpu
            # Run container (CPU version)
            docker run -it \
              -p 6080:6080 \
              -p 8888:8888 \
              --mount source=cognitive-robotics-opt-volume,target=/opt \
            Reconstruction of OpenAI spinningup for reinforcement-learning
            Pythondot img2Lines of Code : 18dot img2no licencesLicense : No License
            copy iconCopy
               ├─env(environment of reacher in unity)
            Installation guide (Now only support linux and macos)
            Pythondot img3Lines of Code : 13dot img3License : Strong Copyleft (GPL-3.0)
            copy iconCopy
            sudo apt-get update && sudo apt-get install libopenmpi-dev
            sudo apt install libgl1-mesa-glx
            conda create -n spinningup python=3.6   #python 3.6 is recommended
            #activate the env
            conda activate spinningup
            # clone my version, I made some chan  
            tensorlayer - tutorial TRPO
            Pythondot img4Lines of Code : 286dot img4License : Non-SPDX
            copy iconCopy
            Trust Region Policy Optimization (TRPO)
            PG method with a large step can collapse the policy performance,
            even with a small step can lead a large differences in policy.
            TRPO constraint the step in policy spa  

            Community Discussions


            Could not install pytorch to my anaconda virtual environment
            Asked 2020-May-19 at 16:36

            I am following the OpenAI's spinningUp tutorial and I stucked in the installation part of the project. I am using Anaconda as said and when I do:



            Answered 2020-May-19 at 14:50

            torch==1.3 on pypi only has files for linux and macOS, see here.

            You will need to install it seperately using the index from the torch website:

            Source https://stackoverflow.com/questions/61893677


            What would be the output from tensorflow dense layer if we assign itself as input and output while making a neural network?
            Asked 2020-Apr-13 at 08:59

            I have been going through the implementation of neural network in openAI code for any Vanilla Policy Gradient (As a matter of fact, this part is used nearly everywhere). The code looks something like this :



            Answered 2020-Apr-13 at 08:59

            Note that this is a discrete action space - there are action_space.n different possible actions at every step, and the agent chooses one.

            To do this the MLP is returning the logits (which are a function of the probabilities) of the different actions. This is specified in the code by + [act_dim] which is appending count of the action_space as the final MLP layer. Note that the last layer of an MLP is the output layer. The input layer is not specified in tensorflow, it is inferred from the inputs.

            tf.random.categorical takes the logits and samples a policy action pi from them, which is returned as a number.

            mlp_categorical_policy also returns logp, the log probability of the action a (used to assign credit), and logp_pi, the log probability of the policy action pi.

            It seems your question is more about the return from the mlp.

            The mlp creates a series of fully connected layers in a loop. In each iteration of the loop, the mlp is creating a new layer using the previous layer x as an input and assigning it's output to overwrite x, with this line x = tf.layers.dense(inputs=x, units=h, activation=activation).

            So the output is not the same as the input, on each iteration x is overwritten with the value of the new layer. This is the same kind of coding trick as x = x + 1, which increments x by 1. This effectively chains the layers together.

            The output of tf.layers.dense is a tensor of size [:,h] where : is the batch dimension (and can usually be ignored). The creation of the last layer happens outisde the loop, it can be seen that the number of nodes in this layer is act_dim (so shape is [:,3]). You can check the shape by doing this:

            Source https://stackoverflow.com/questions/61163513

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


            No vulnerabilities reported

            Install spinningup

            You can download it from GitHub.
            You can use spinningup 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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