deep-q-learning | Minimal Deep Q Learning implementations | Machine Learning library

by keon Python Version: Current License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | deep-q-learning Summary

deep-q-learning is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras applications. deep-q-learning 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.

Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

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deep-q-learning has a medium active ecosystem.

It has 1212 star(s) with 453 fork(s). There are 62 watchers for this library.

It had no major release in the last 6 months.

There are 16 open issues and 8 have been closed. On average issues are closed in 56 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of deep-q-learning is current.

Quality

deep-q-learning has 0 bugs and 0 code smells.

Security

deep-q-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

deep-q-learning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

deep-q-learning 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-q-learning 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.

Top functions reviewed by kandi - BETA

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

Play the model .
Initialize the model .
Build the model .
Compute thehuberberg - loss .
Compute the action given a state .
Copy weights from model to target model
Loads weights from the model .
Memorizes a state .
Save weights to file .

Get all kandi verified functions for this library.

deep-q-learning Key Features

No Key Features are available at this moment for deep-q-learning.

deep-q-learning Examples and Code Snippets

Nagging-Naagin: Deep Q-Learning Applied to Snake,Usage

Python

Lines of Code : 31

License : No License

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usage: snakeGame.py [-h] [-a {ReflexAgent,MinMaxAgent}]
                    [-s {RandomFoodAgent,MaxManhattanFoodAgent}] [-n] [-t]
                    [-f FRAMERATE] [-z] [-m SIMULATE] [-y NUM_THREADS]
                    [-d DEPTH] [-c]

Naagin-Nagg

2. Implement Learning Algorithm

Python

Lines of Code : 24

License : No License

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# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, random_seed).to(device)
self.actor_target = Actor(state_size, action_size, random_seed).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),

Using Deep Q-Network to Learn How To Play Flappy Frog,Deep Q-Network Algorithm

Python

Lines of Code : 16

License : Strong Copyleft (GPL-3.0)

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Initialize replay memory D to size N
Initialize action-value function Q with random weights
for episode = 1, M do
    Initialize state s_1
    for t = 1, T do
        With probability ϵ select random action a_t
        otherwise select a_t=max_a  Q(s

Community Discussions

Trending Discussions on deep-q-learning

OpenAI-Gym and Keras-RL: DQN expects a model that has one dimension for each action

QUESTION

OpenAI-Gym and Keras-RL: DQN expects a model that has one dimension for each action

Asked 2022-Mar-02 at 10:55

I am trying to set a Deep-Q-Learning agent with a custom environment in OpenAI Gym. I have 4 continuous state variables with individual limits and 3 integer action variables with individual limits.

Here is the code:

...

ANSWER

Answered 2021-Dec-23 at 11:19

As we talked about in the comments, it seems that the Keras-rl library is no longer supported (the last update in the repository was in 2019), so it's possible that everything is inside Keras now. I take a look at Keras documentation and there are no high-level functions to build a reinforcement learning model, but is possible to use lower-level functions to this.

Here is an example of how to use Deep Q-Learning with Keras: link

Another solution may be to downgrade to Tensorflow 1.0 as it seems the compatibility problem occurs due to some changes in version 2.0. I didn't test, but maybe the Keras-rl + Tensorflow 1.0 may work.

There is also a branch of Keras-rl to support Tensorflow 2.0, the repository is archived, but there is a chance that it will work for you

Source https://stackoverflow.com/questions/70261352

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

Vulnerabilities

No vulnerabilities reported

Install deep-q-learning

You can download it from GitHub.
You can use deep-q-learning 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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