policy
TRPO-and-POP3D
MSanjive · PyTorch
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Overview
Name
TRPO-and-POP3D
Author
MSanjive
Framework
PyTorch
License
unknown
Skill type
navigation
Evidence level
untested
Task description
This project explores advanced methods and/or applications in Reinforcement Learning. In this project, Proximal Policy Optimisation and a variant of Trust Region Policy Optimisation (TRPO) - Policy Optimisation with Penalised Point Probability Distance (POP3D) was used to train the LunarLander and C
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
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Datasets that reference this policy
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