policy-gradient | Minimal Monte Carlo Policy Gradient Algorithm | Reinforcement Learning library

by keon Python Version: Current License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(1)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 Permissive License and it has low support. However policy-gradient build file is not available. You can download it from GitHub.

Minimal implementation of Stochastic Policy Gradient Algorithm in Keras.

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Quality

Security

License

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Support

policy-gradient has a low active ecosystem.

It has 152 star(s) with 43 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 2 have been closed. On average issues are closed in 346 days. 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 0 bugs and 0 code smells.

Security

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

policy-gradient code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

policy-gradient 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

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.

policy-gradient saves you 38 person hours of effort in developing the same functionality from scratch.

It has 101 lines of code, 9 functions and 1 files.

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

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.

Train the model
Compute discounted rewards
Convert a tensor to a numpy array
Compute action and probability of action
Recognize a given action
Loads weights from the model
Saves weights to file

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

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

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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