policy
Unity-MLAgents-3DBall-PPO
KunalKhadgi · PyTorch
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Overview
Name
Unity-MLAgents-3DBall-PPO
Author
KunalKhadgi
Framework
PyTorch
License
Apache-2.0
Skill type
other
Evidence level
untested
Task description
The `3DBall` environment is a reinforcement learning task designed to test an agent's ability to learn continuous control. This project uses the **Proximal Policy Optimization (PPO)** algorithm, widely-used deep RL method, to train the agent. The final `.onnx` model, the trained nn, is included in t
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 Unity-MLAgents-3DBall-PPO yet.
Datasets that reference this policy
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