RocketLander VS GnomansLand

Compare RocketLander vs GnomansLand and see what are their differences.

RocketLander

A simple framework equipped with optimization algorithms, such as reinforcement learning, evolution strategies, genetic optimization, and simulated annealing, to enable an orbital rocket booster to land autonomously. (by kaifishr)
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RocketLander GnomansLand
1 4
1 16
- -
1.7 0.0
about 1 year ago about 1 year ago
Python Python
MIT License MIT License
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.

RocketLander

Posts with mentions or reviews of RocketLander. We have used some of these posts to build our list of alternatives and similar projects.

GnomansLand

Posts with mentions or reviews of GnomansLand. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing RocketLander and GnomansLand you can also consider the following projects:

SpaceDrones - A simple learning environment with space drones for evolution-inspired optimization.

Masendor - Open source educational and historical battle action game, All helps accepted

Pendulum - Inverted Pendulum in python with pybox2d. Fuzzy and PID simulation included.

unicast_network_sim - Ludum Dare 53 game jam entry

PyGame-Learning-Environment - PyGame Learning Environment (PLE) -- Reinforcement Learning Environment in Python.

Gradient-Free-Optimizers - Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

Agar.io_Q-Learning_AI - An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available actions