DEEP-REINFORCEMENT-LEARNING-NANODEGREE VS MPO

Compare DEEP-REINFORCEMENT-LEARNING-NANODEGREE vs MPO and see what are their differences.

MPO

Pytorch implementation of "Maximum a Posteriori Policy Optimization" with Retrace for Discrete gym environments (by acyclics)
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DEEP-REINFORCEMENT-LEARNING-NANODEGREE MPO
1 2
1 23
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10.0 10.0
almost 4 years ago over 3 years ago
Jupyter Notebook Python
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The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

DEEP-REINFORCEMENT-LEARNING-NANODEGREE

Posts with mentions or reviews of DEEP-REINFORCEMENT-LEARNING-NANODEGREE. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-19.

MPO

Posts with mentions or reviews of MPO. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-19.

What are some alternatives?

When comparing DEEP-REINFORCEMENT-LEARNING-NANODEGREE and MPO you can also consider the following projects:

acme - A library of reinforcement learning components and agents

go-opencv - Go bindings for OpenCV / 2.x API in gocv / 1.x API in opencv

Primitive Pictures - Reproducing images with geometric primitives.

ln - 3D line art engine.

gg - Go Graphics - 2D rendering in Go with a simple API.

gowitness - 🔍 gowitness - a golang, web screenshot utility using Chrome Headless