TF_RL | Eagerly Experimentable!!! | Machine Learning library

by Rowing0914 Python Version: Current License: MIT

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kandi X-RAY | TF_RL Summary

TF_RL is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. TF_RL 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.

This is the repo for implementing and experimenting the variety of RL algorithms using Tensorflow Eager Execution. And, since our Lord Google gracefully allows us to use their precious GPU resources without almost restriction, I have decided to enable most of codes run on Google Colab. So, if you don't have GPUs, please feel free to try it out on Google Colab. Note: As it is known that Eager mode takes time than Graph Execution in general so that in this repo, I use Eager for debugging and Graph mode for training!!! The beauty of eager mode come here!! we can flexibly switch eager mode and graph mode with minimal modification(@tf.contrib.eager.defun), pls check the link.

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TF_RL has a low active ecosystem.

It has 18 star(s) with 1 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

TF_RL has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TF_RL is current.

Quality

TF_RL has 0 bugs and 0 code smells.

Security

TF_RL has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

TF_RL code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

TF_RL 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

TF_RL 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.

TF_RL saves you 6813 person hours of effort in developing the same functionality from scratch.

It has 14129 lines of code, 811 functions and 243 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Return a random storage index
Store an episode
Append data pairs
Performs a pretraining with the given policy
Sample from the model
Sample the probability distribution of a given size
Add an item to the heap
Move agent
Train DDPG on policy
Train the DRQN
Perform a DQFD
Train DQN per step
A train DDPG
Train a TFNQN_DQN
Train a DQN
Train a simulated agent
Pre - train the agent without priorization
Train the DQN algorithm
Train double DQN
Train a TRPO model
Run the model
Explains an environment
Advances the agent
Start training
Plot plot

Get all kandi verified functions for this library.

TF_RL Key Features

No Key Features are available at this moment for TF_RL.

TF_RL Examples and Code Snippets

No Code Snippets are available at this moment for TF_RL.

Community Discussions

Trending Discussions on TF_RL

DQN algorithm does not converge on CartPole-v0

QUESTION

DQN algorithm does not converge on CartPole-v0

Asked 2019-Apr-06 at 19:56

Short Description of my model

I am trying to write my own DQN algorithm in Python, using Tensorflow following the paper(Mnih et al., 2015). In train_DQN function, I have defined the training procedure, and DQN_CartPole is for defining the function approximation(simple 3-layered Neural Network). For loss function, Huber loss or MSE is implemented followed by the gradient clipping(between -1 and 1). Then, I have implemented soft-update method instead of hard-update of the target network by copying the weights in the main network.

Question

I am trying it on the CartPole environment(OpenAI gym), but the rewards does not improve as it does in other people's algorithms, such as keras-rl. Any help will be appreciated.

reward over timestep

If possible, could you have a look at the source code?

...

ANSWER

Answered 2019-Apr-06 at 19:33

Briefly looking over, it seems that the dones variable is a binary vector where 1 denotes done, and 0 denotes not-done.

You then use dones here:

Source https://stackoverflow.com/questions/55552366

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

Vulnerabilities

No vulnerabilities reported

Install TF_RL

Install from Pypi(Test)
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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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