BipedalWalker-v2 | Solving openAI 's game 'BipedalWalker-v2 ' with Deep | Machine Learning library

by Kyziridis Python Version: Current License: GPL-2.0

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | BipedalWalker-v2 Summary

BipedalWalker-v2 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. BipedalWalker-v2 has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However BipedalWalker-v2 build file is not available. You can download it from GitHub.

Solving openAI's game 'BipedalWalker-v2' with Deep Reinforcement Learning

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BipedalWalker-v2 has a low active ecosystem.

It has 14 star(s) with 8 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of BipedalWalker-v2 is current.

Quality

BipedalWalker-v2 has 0 bugs and 139 code smells.

Security

BipedalWalker-v2 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

BipedalWalker-v2 code analysis shows 0 unresolved vulnerabilities.

There are 3 security hotspots that need review.

License

BipedalWalker-v2 is licensed under the GPL-2.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

BipedalWalker-v2 releases are not available. You will need to build from source code and install.

BipedalWalker-v2 has no build file. You will be need to create the build yourself to build the component from source.

BipedalWalker-v2 saves you 1233 person hours of effort in developing the same functionality from scratch.

It has 2774 lines of code, 129 functions and 14 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed BipedalWalker-v2 and discovered the below as its top functions. This is intended to give you an instant insight into BipedalWalker-v2 implemented functionality, and help decide if they suit your requirements.

Train the network
Returns discounted rewards
Run the optimizer
Train actor
Get a single batch of action
Constructs an Actor
Actor loss function
Computes the Poisson loss function
Calculate the actor loss
Build the model
Proximal policy loss function
Evaluate the model
Compute a Critic loss function
Choose an action
Evaluate the given state
Updates the observation weights
Generate random sample
Calculate the norm of x
Updates the model with the given data
Store an observation
Adds a histogram
Calculate the discount of x
Compute discounted rewards
Trains the model
Run the episode
Build an activation model
Save the current configuration to disk

Get all kandi verified functions for this library.

BipedalWalker-v2 Key Features

No Key Features are available at this moment for BipedalWalker-v2.

BipedalWalker-v2 Examples and Code Snippets

No Code Snippets are available at this moment for BipedalWalker-v2.

Community Discussions

Trending Discussions on BipedalWalker-v2

Tensorflow: Different results with the same random seed

Ray Tensorflow-gpu 2.0 RecursionError

Why would setting "export OPENBLAS_NUM_THREADS=1" impair the performance?

QUESTION

Tensorflow: Different results with the same random seed

Asked 2019-Sep-26 at 00:19

I'm running a reinforcement learning program in a gym environment(BipedalWalker-v2) implemented in tensorflow. I've set the random seed of the environment, tensorflow and numpy manually as follows

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ANSWER

Answered 2019-Jan-05 at 03:35

It seems that tensorflow neural networks introduce randomness during training that isn't controlled by a numpy random seed. The randomness appears to possibly come from python hash operations and parallelized operations executing in non-controlled ordering, at the very least.

I had success getting 100% reproducibility using a keras-tensorflow NN, by following the setup steps in this response: How to get reproducible results in keras

specifically, I used the formulation proposed by @Poete Maudit in that link.

They key was to set random seed values UP FRONT, for numpy, python, and tensorflow, then also to make tensorflow run on single-thread CPU in a specially-configured session.

Here's the code i used, updated very slightly from the link i posted.

Source https://stackoverflow.com/questions/54047654

QUESTION

Ray Tensorflow-gpu 2.0 RecursionError

Asked 2019-Sep-03 at 21:32

System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04

Ray installed from (source or binary): binary

Ray version: 0.7.3

Python version: 3.7

Tensorflow version: tensorflow-gpu 2.0.0rc0

Exact command to reproduce:

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ANSWER

Answered 2019-Sep-02 at 17:51

The core problem appears to be that cloudpickle (which Ray uses to serialize remote functions and ship them to the worker processes) isn't able to pickle the tf.keras.Sequential class. For example, I can reproduce the issue as follows

Source https://stackoverflow.com/questions/57750920

QUESTION

Why would setting "export OPENBLAS_NUM_THREADS=1" impair the performance?

Asked 2019-May-23 at 10:21

I try to set "export OPENBLAS_NUM_THREADS=1" as this document suggests. But I found a strange phenomenon that setting this significantly impairs the performance of my RL algorithms(I've done some tests for TD3 and SAC, all results consistently indicate that "export OPENBLAS_NUM_THREADS=1" impairs the performance). Why would this cause such a big problem?

BTW, the algorithms are implemented using Tensorflow1.13, data are fed into the neural network through tf.data.Dataset. all tests are done on BipedalWalker-v2 environment from OpenAI's Gym.

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ANSWER

Answered 2019-May-23 at 10:21

The linked guide suggests setting this variable specifically when using ray, not always.

AFAICS, that's because that framework itself spawns many processes (one for each actor or something), so each of them using multiple threads would bring no speedup. This is not the case when there's only one or only a few processes.

On a general note, OpenBLAS FAQ says that OpenBLAS' multithreading might "conflict" with the main program's multithreading and recommends setting OPENBLAS_NUM_THREADS=1 in such a case. The FAQ entry however fails to provide any details to verify its claim, so it can very well be obsolete! As per https://github.com/obspy/obspy/wiki/Notes-on-Parallel-Processing-with-Python-and-ObsPy, symptoms of such a "conflict" are rampant deadlocks and segfaults. So if you have nothing of the kind, you are in the clear. Major Python libraries are very responsible in dealing with such problem themselves rather than dumping them on the user, so I'm pretty sure that if OpenBLAS has any usage restrictions, numpy and scipy enforce them internally and automatically if you are using OpenBLAS through them.

Source https://stackoverflow.com/questions/56104472

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

Vulnerabilities

No vulnerabilities reported

Install BipedalWalker-v2

You can download it from GitHub.
You can use BipedalWalker-v2 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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