policy
Train-SAC-Agent-for-Ball-Balance-Control-Using-Reinforcement-Learning-
youssefx99 · PyTorch
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Overview
Name
Train-SAC-Agent-for-Ball-Balance-Control-Using-Reinforcement-Learning-
Author
youssefx99
Framework
PyTorch
License
unknown
Skill type
other
Evidence level
untested
Task description
The training goal is to train a soft actor-critic (SAC) reinforcement learning agent to control a robot arm for a ball-balancing task. The robot arm in this example is a Kinova Gen3 robot, which is a seven degree of-freedom (DOF).
Spaces
Action space
other · 0-dim · 0Hz
Observation space
- type: other
Links
HuggingFace repo
null
Paper (arXiv)
null
Compatible robots
3+17 mentioned but not in catalog yetCompatible environments
0No environments list Train-SAC-Agent-for-Ball-Balance-Control-Using-Reinforcement-Learning- yet.
Datasets that reference this policy
0No datasets reference Train-SAC-Agent-for-Ball-Balance-Control-Using-Reinforcement-Learning- yet.