policy-gradient | Policy gradient reinforcement | Reinforcement Learning library

by JuliusKunze Python Version: Current License: GPL-3.0

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

kandi X-RAY | policy-gradient Summary

policy-gradient is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Deep Learning, Tensorflow, Keras applications. policy-gradient has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However policy-gradient build file is not available. You can download it from GitHub.

Policy gradient reinforcement learning in modern Tensorflow (Keras/Probability/Eager)

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policy-gradient has a low active ecosystem.

It has 4 star(s) with 0 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of policy-gradient is current.

Quality

policy-gradient has no bugs reported.

Security

policy-gradient has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

policy-gradient is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

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

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

Performs a HalfCheetah model .

Get all kandi verified functions for this library.

policy-gradient Key Features

No Key Features are available at this moment for policy-gradient.

policy-gradient Examples and Code Snippets

Quick Start

pypi

Lines of Code : 42

License : No License

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import gym, torch, numpy as np, torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
import tianshou as ts


task = 'CartPole-v0'
lr, epoch, batch_size = 1e-3, 10, 64
train_num, test_num = 10, 100
gamma, n_step, target_freq = 0.9, 3, 320

Community Discussions

Trending Discussions on policy-gradient

Unable to start ipython kernel Python 3.8.5

AttributeError: 'function' object has no attribute 'predict'. Keras

Best way to bound outputs from neural networks on reinforcement learning

QUESTION

Unable to start ipython kernel Python 3.8.5

Asked 2021-Jan-09 at 10:36

I am unable to start my notebook on my newly installed python environment. The kernel fails to start giving me this error:

...

ANSWER

Answered 2021-Jan-09 at 10:36

I installed notebook for my main python (not in virtual environment) and found out that the problem occurred only when I was starting a notebook using the python from my virtual environment.

So I followed instructions in this link: https://janakiev.com/blog/jupyter-virtual-envs/

In my virtual environment, I only runned pip install ipykernel and now it works.

The weird thing is that now I can run notebooks in other virtual environments without installing ipykernel in them. I guess installing ipykernel in my first virtual environment changed something in my main notebook installation and now it works for all. Maybe someone could explain it better than me though.

Anyway problem solved for me!

Source https://stackoverflow.com/questions/65628115

QUESTION

AttributeError: 'function' object has no attribute 'predict'. Keras

Asked 2019-Nov-05 at 07:42

I am working on an RL problem and I created a class to initialize the model and other parameters. The code is as follows:

...

ANSWER

Answered 2019-Nov-04 at 13:09

in last code block,

Source https://stackoverflow.com/questions/58693786

QUESTION

Best way to bound outputs from neural networks on reinforcement learning

Asked 2018-Aug-05 at 10:50

I am training a neural network (feedforward, Tanh hidden layers) that receives states as inputs and gives actions as outputs. I am following the REINFORCE algorithm for policy-gradient reinforcement learning.

However, I need my control actions to be bounded (let us say from 0-5). Currently the way I am doing this is by using a sigmoid output function and multiplying the output by 5. Although my algorithm has a moderate performance, I find the following drawback from using this “bounding scheme” for the output:

I know for regression (hence I guess for reinforcement learning) a linear output is best, and although the sigmoid has a linear part I am afraid the network has not been able to capture this linear output behaviour correctly, or it captures it way too slowly (as its best performance is for classification, therefore polarizing the output).

I am wondering what other alternatives there are, and maybe some heuristics on the matter.

...

ANSWER

Answered 2018-Aug-05 at 10:50

Have you considered using nn.ReLU6()? This is a bounded version of the rectified linear unit, which output is defined as

Source https://stackoverflow.com/questions/51693567

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

Vulnerabilities

No vulnerabilities reported

Install policy-gradient

You can download it from GitHub.
You can use policy-gradient 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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