end-to-end-negotiator | End Learning for Negotiation Dialogues | Reinforcement Learning library

by facebookresearch Python Version: Current License: Non-SPDX

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

kandi X-RAY | end-to-end-negotiator Summary

end-to-end-negotiator is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Deep Learning, Pytorch applications. end-to-end-negotiator has no bugs, it has no vulnerabilities and it has medium support. However end-to-end-negotiator build file is not available and it has a Non-SPDX License. You can download it from GitHub.

(2) [Deal or No Deal? End-to-End Learning for Negotiation Dialogues] The code is developed by [Facebook AI Research] The code trains neural networks to hold negotiations in natural language, and allows reinforcement learning self play and rollout-based planning.

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Support

end-to-end-negotiator has a medium active ecosystem.

It has 1364 star(s) with 278 fork(s). There are 97 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 21 have been closed. On average issues are closed in 31 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of end-to-end-negotiator is current.

Quality

end-to-end-negotiator has 0 bugs and 0 code smells.

Security

end-to-end-negotiator has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

end-to-end-negotiator code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

end-to-end-negotiator has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

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

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Update the model
Forward LM
Forward a word embedding
Forward selection of embeddings
Run the agent
Dump the given string to stdout
Return a dict of metrics
Splits a dataset into batches
Make a list of Tensor s
Write a word embedding
Tokenize a file
Write embedding
Forward validation
Update the objective function
Calculates the total loss for validation
Marginal validation
Train a single pass
Read a dataset from a text file
Calculates the validation loss
Perform the forward loss
Perform forward encoder
Forward KL divergence
Compute the word embedding
Write batch to Tensorboard
Evaluate the validation function
Plot strategy

Get all kandi verified functions for this library.

end-to-end-negotiator Key Features

No Key Features are available at this moment for end-to-end-negotiator.

end-to-end-negotiator Examples and Code Snippets

No Code Snippets are available at this moment for end-to-end-negotiator.

Community Discussions

Trending Discussions on end-to-end-negotiator

Package Cuda 8 not found for PyTorch

QUESTION

Package Cuda 8 not found for PyTorch

Asked 2018-Sep-28 at 09:20

I am trying to follow the tutorial for chatbots at facebook (https://github.com/facebookresearch/end-to-end-negotiator) and I am stuck here : conda install pytorch torchvision cuda80 -c soumith I managed to get all packages except for cuda80 which is not found. I also tried conda install magma-cuda80 -c soumith conda install cuda80 -c soumith conda install magma-cuda80 -c pytorch

with no success. PackagesNotFoundError: The following packages are not available from current channels: - magma-cuda80

Where can I find the package ? I am on Windows10.

...

ANSWER

Answered 2018-Feb-19 at 21:18

Open Anaconda Prompt and run -

conda install -c peterjc123 pytorch cuda80

conda install -c peterjc123 pytorch cuda90

Source https://stackoverflow.com/questions/48874285

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

Vulnerabilities

No vulnerabilities reported

Install end-to-end-negotiator

All code was developed with Python 3.0 on CentOS Linux 7, and tested on Ubuntu 16.04. In addition, we used PyTorch 1.0.0, CUDA 9.0, and Visdom 0.1.8.4.

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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