robo-gym
football
| robo-gym | football | |
|---|---|---|
| 4 | 2 | |
| 371 | 3,253 | |
| 0.0% | 0.6% | |
| 0.0 | 0.0 | |
| about 2 years ago | 6 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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robo-gym
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Simulating robotic arm for object manipulation
what about robo-gym https://github.com/jr-robotics/robo-gym?
- Joanneum Research Institute Release Version 1.0.0 Of Robo-Gym, An Open Source Toolkit For Distributed Deep Reinforcement Learning On Real And Simulated Robots
football
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Creating a new football game
For fun, merging such an idea with Google's open source football research project and its AI could result in a very interesting game!
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How to find which Manjaro package corresponds to which Debian packages?
So I switched to Manjaro from Ubuntu quite some time ago and I am loving the decision whenever I have to build an application there are a bunch of required packages that the application to build is dependent on. But as Debian-based distros are the most popular, the packages are listed as in the Debian Repository. So is there a way I can find which package in Manjaro Repository corresponds to the one in the Debian Repositories as the names of the packages are usually different. Like I was recently trying to install google-research football on my machine and there were the following packages listed as dependencies :-
What are some alternatives?
IsaacGymEnvs - Isaac Gym Reinforcement Learning Environments
space-gym - Challenging reinforcement learning environments with locomotion tasks in space
drl_grasping - Deep Reinforcement Learning for Robotic Grasping from Octrees
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
rlcard - Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
dmc2gymnasium - Gymnasium integration for the DeepMind Control (DMC) suite
modelicagym - Modelica models integration with Open AI Gym
ai-economist - Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcement learning to learn optimal economic policies, as done by the AI Economist (https://www.einstein.ai/the-ai-economist).
MuJoCo_RL_UR5 - A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.
Unity-Robotics-Hub - Central repository for tools, tutorials, resources, and documentation for robotics simulation in Unity.