gym-hybrid
Collection of OpenAI parametrized action-space environments. (by thomashirtz)
gymprecice
A framework to design and develop reinforcement learning environments for single- and multi-physics active flow control. (by gymprecice)
| gym-hybrid | gymprecice | |
|---|---|---|
| 1 | 1 | |
| 52 | 20 | |
| - | - | |
| 0.0 | 7.4 | |
| about 1 year ago | 3 months ago | |
| Python | Python | |
| - | 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.
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.
gym-hybrid
Posts with mentions or reviews of gym-hybrid.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Best way to define hybrid environment action space
Link to the issue and the code I wrote
gymprecice
Posts with mentions or reviews of gymprecice.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Interesting Physics related RL gym?
Please check our recently released package https://github.com/gymprecice/gymprecice. There are couple of example there for active flow control and FSI. Gym-preCICE is a Python preCICE adapter fully compliant with Gymnasium (also known as OpenAI Gym) API to facilitate designing and developing Reinforcement Learning (RL) environments for single- and multi-physics active flow control (AFC) applications. In an actor-environment setting, Gym-preCICE takes advantage of preCICE, an open-source coupling library for partitioned multi-physics simulations, to handle information exchange between a controller (actor) and an AFC simulation environment. The developed framework results in a seamless non-invasive integration of realistic physics-based simulation toolboxes with RL algorithms.
What are some alternatives?
When comparing gym-hybrid and gymprecice you can also consider the following projects:
modelicagym - Modelica models integration with Open AI Gym
MO-Gymnasium - Multi-objective Gymnasium environments for reinforcement learning
RayEnvWrapper - OpenAi's gym environment wrapper to vectorize them with Ray
Minari - A standard format for offline reinforcement learning datasets, with popular reference datasets and related utilities
rex-gym - OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
gym-simplegrid - Simple Gridworld Gymnasium Environment
gym-cartpole-swingup - A simple, continuous-control environment for OpenAI Gym
Gym-Stag-Hunt - A custom reinfrocement learning environment for OpenAI Gym & PettingZoo that implements various Stag Hunt-like social dilemma games.