Reinforcement-learning-with-tensorflow | Simple Reinforcement learning tutorials , 莫烦Python 中文AI教学 | Reinforcement Learning library
kandi X-RAY | Reinforcement-learning-with-tensorflow Summary
kandi X-RAY | Reinforcement-learning-with-tensorflow Summary
Reinforcement-learning-with-tensorflow is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Deep Learning, Pytorch applications. Reinforcement-learning-with-tensorflow has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However Reinforcement-learning-with-tensorflow build file is not available. You can download it from GitHub.
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
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Reinforcement-learning-with-tensorflow has a medium active ecosystem.
It has 8122 star(s) with 4955 fork(s). There are 290 watchers for this library.
It had no major release in the last 6 months.
There are 63 open issues and 126 have been closed. On average issues are closed in 16 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Reinforcement-learning-with-tensorflow is current.
Quality
Reinforcement-learning-with-tensorflow has 0 bugs and 0 code smells.
Security
Reinforcement-learning-with-tensorflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Reinforcement-learning-with-tensorflow code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Reinforcement-learning-with-tensorflow is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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Reinforcement-learning-with-tensorflow releases are not available. You will need to build from source code and install.
Reinforcement-learning-with-tensorflow has no build file. You will be need to create the build yourself to build the component from source.
Top functions reviewed by kandi - BETA
kandi has reviewed Reinforcement-learning-with-tensorflow and discovered the below as its top functions. This is intended to give you an instant insight into Reinforcement-learning-with-tensorflow implemented functionality, and help decide if they suit your requirements.
- Train the machine
- Chooses an action based on s
- Render the camera
- Move motor by given action
- Sample from the tree
- Update environment
- Reset the observation
- Move the agent
- Perform a work
- Choose a random action from sess
- Runmaze
- Choose action based on given observation
- Render the robot
- Choose an action given an observation
- Add new priority
- Adds gradients to the graph
- Choose action for given observation
- Evaluate the environment
- Train on episode
- Move the motor by the given action
- Train the model
- Returns the leaf at v
- Sample the priors from the tree
- Run RL loop
- Update the actor
- Convert point to line segment
- Select the action given an observation
- Builds the network
- Choose a random action
Get all kandi verified functions for this library.
Reinforcement-learning-with-tensorflow Key Features
No Key Features are available at this moment for Reinforcement-learning-with-tensorflow.
Reinforcement-learning-with-tensorflow Examples and Code Snippets
Implementation of Distributed Reinforcement Learning with Tensorflow,How to Run
Python
Lines of Code : 28
License : No License
Lines of Code : 28License : No License
CopyCUDA_VISIBLE_DEVICES=-1 python train_a3c.py --job_name --job_name actor --task 0
CUDA_VISIBLE_DEVICES=-1 python train_a3c.py --job_name --job_name actor --task 0
CUDA_VISIBLE_DEVICES=-1 python train_a3c.py --job_name --job_name actor --task 1
CUDA_V Implementation of Distributed Reinforcement Learning with Tensorflow,Dependency
PythonLines of Code : 4License : No License
Lines of Code : 4License : No LicenseCopyopencv-python
gym[atari]
tensorboardX
tensorflow==1.14.0
Reinforcement Learning Library,Usage
PythonLines of Code : 3License : No License
Lines of Code : 3License : No LicenseCopyagent = A3CAgent(num_actions, lambda: model)
agent.train(env_name)
tensorboard --logdir=out --reload_interval=2
tensorlayer - tutorial A3 C
PythonLines of Code : 211License : Non-SPDX
Lines of Code : 211License : Non-SPDXCopy"""
Asynchronous Advantage Actor Critic (A3C) with Continuous Action Space.
Actor Critic History
----------------------
A3C > DDPG (for continuous action space) > AC
Advantage
----------
Train faster and more stable than AC.
Disadvantage
--- tensorlayer - tutorial DQN
PythonLines of Code : 96License : Non-SPDX
Lines of Code : 96License : Non-SPDXCopy"""
Deep Q-Network Q(a, s)
-----------------------
TD Learning, Off-Policy, e-Greedy Exploration (GLIE).
Q(S, A) <- Q(S, A) + alpha * (R + lambda * Q(newS, newA) - Q(S, A))
delta_w = R + lambda * Q(newS, newA)
See David Silver RL Tutorial Le tensorlayer - tutorial Qlearning
PythonLines of Code : 76License : Non-SPDX
Lines of Code : 76License : Non-SPDXCopy"""Q-Table learning algorithm.
Non deep learning - TD Learning, Off-Policy, e-Greedy Exploration
Q(S, A) <- Q(S, A) + alpha * (R + lambda * Q(newS, newA) - Q(S, A))
See David Silver RL Tutorial Lecture 5 - Q-Learning for more details.
For Q-Ne Community Discussions
Trending Discussions on Reinforcement-learning-with-tensorflow
QUESTION
How to size divs so they don't appear too small on mobile devices?
Asked 2017-Jan-19 at 22:46
I am designing a page for a blog. The page has a fixed position sidebar, and a centered div for content. You can see it here. Here's my CSS:
...ANSWER
Answered 2017-Jan-19 at 22:44you can set a min-width in your .content with any value you may like, just remember you have a fixed sidebar
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Reinforcement-learning-with-tensorflow
You can download it from GitHub.
You can use Reinforcement-learning-with-tensorflow 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.
You can use Reinforcement-learning-with-tensorflow 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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