modelicagym
gym-hybrid
| modelicagym | gym-hybrid | |
|---|---|---|
| 1 | 1 | |
| 73 | 46 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 2 years ago | about 1 year ago | |
| Python | Python | |
| GNU General Public License v3.0 only | - |
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modelicagym
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control libraries for python
Modelica-gym can be used if you are implementing model separately in Modelica language
gym-hybrid
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Best way to define hybrid environment action space
Link to the issue and the code I wrote
What are some alternatives?
ns3-gym - ns3-gym - The Playground for Reinforcement Learning in Networking Research
gymprecice - A framework to design and develop reinforcement learning environments for single- and multi-physics active flow control.
robo-gym - An open source toolkit for Distributed Deep Reinforcement Learning on real and simulated robots.
RayEnvWrapper - OpenAi's gym environment wrapper to vectorize them with Ray
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
rex-gym - OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
Minigrid - Simple and easily configurable grid world environments for reinforcement learning
space-gym - Challenging reinforcement learning environments with locomotion tasks in space
gym-cartpole-swingup - A simple, continuous-control environment for OpenAI Gym
bark-ml - Gym environments and agents for autonomous driving.