rl-tutorial | Source code for A deep dive into reinforcement learning | Machine Learning library

by AdamStelmaszczyk Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | rl-tutorial Summary

rl-tutorial is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. rl-tutorial has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Solving Mountain Car environment with TensorFlow & Keras implementation of DQN. For similar code solving some Atari games, look here.

Support

Quality

Security

License

Reuse

Support

rl-tutorial has a low active ecosystem.

It has 12 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

rl-tutorial has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of rl-tutorial is current.

Quality

rl-tutorial has no bugs reported.

Security

rl-tutorial has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

rl-tutorial 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

rl-tutorial 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.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Train a model
Evaluate a model
Returns a random sample of data
Fit a batch model to target_model
Create a keras model
Encode a sample
Adds an observation to the reservoir
Generate a greedy action
Save an image
Save model to file
Create one - hot encode
Log a scalar
Compute the next value of an observation
Predict the given model
Return random samples from the stream
Load a keras model
Set random seed

Get all kandi verified functions for this library.

rl-tutorial Key Features

No Key Features are available at this moment for rl-tutorial.

rl-tutorial Examples and Code Snippets

No Code Snippets are available at this moment for rl-tutorial.

Community Discussions

Trending Discussions on rl-tutorial

What does the 'Array => [...]' notation mean

libcurl and curl_global_init() on multiple executable files

QUESTION

What does the 'Array => [...]' notation mean

Asked 2019-Jul-07 at 10:55

In some Perl code I have been asked to maintain, I encountered the following construct:

...

ANSWER

Answered 2019-Jul-07 at 10:55

You mention hashing and hashes, but neither are involved.

Source https://stackoverflow.com/questions/56921339

QUESTION

libcurl and curl_global_init() on multiple executable files

Asked 2017-Feb-15 at 07:48

I have to realize a bash script which perform 2 request in a loop with libcurl to a webserver.

The script has this structure:

...

ANSWER

Answered 2017-Feb-15 at 07:48

In your case callA and callB are two distinctly separate programs running in one process each and thus totally independent of each other.

They have one "life time" each and thus both should call curl_global_init().

Source https://stackoverflow.com/questions/42228664

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

Vulnerabilities

No vulnerabilities reported

Install rl-tutorial

There is an automatic build on Travis which does the same.
Clone this repo: git clone https://github.com/AdamStelmaszczyk/rl-tutorial.git.
Install conda for dependency management.
Create tutorial conda environment: conda create -n tutorial python=3.6.5 -y.
Activate tutorial conda environment: source activate tutorial. All the following commands should be run in the activated tutorial environment.
Install basic dependencies: pip install -r requirements.txt.

Support

Playing Atari with Deep Reinforcement Learning, https://www.cs.toronto.edu/~vmnih/docs/dqn.pdfHuman-level control through deep reinforcement learning, https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdfMountainCar description: https://github.com/openai/gym/wiki/MountainCar-v0MountainCar source code: https://github.com/openai/gym/blob/4c460ba6c8959dd8e0a03b13a1ca817da6d4074f/gym/envs/classic_control/mountain_car.py

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