policy
fishing-for-best-returns
AishwaryaVathada · PyTorch
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Overview
Name
fishing-for-best-returns
Author
AishwaryaVathada
Framework
PyTorch
License
MIT
Skill type
other
Evidence level
untested
Task description
Reinforcement learning framework for long-horizon safe control in sustainable fishery management. Implements PPO, SAC, TD3, distributional RL (TQC), constrained PPO (primal-dual Lagrangian), and evolution strategies, with Optuna hyperparameter tuning and Monte Carlo policy verification under uncerta
Spaces
Action space
other · 0-dim · 0Hz
Observation space
- type: other
Links
HuggingFace repo
null
Paper (arXiv)
null
Compatible robots
20anybotics-anymal-cnot in seedalohanot in seedgoogle-barkour-vbnot in seedboston-dynamics-spotnot in seedfranka-fr3not in seedgoogle-barkour-v0not in seedagilex-pipernot in seedberkeley-humanoidnot in seedbitcraze-crazyflie-2not in seedanybotics-anymal-bnot in seedagility-cassienot in seedarx-l5not in seedbooster-t1not in seedfranka-emika-pandanot in seedfranka-fr3-v2not in seeddynamixel-2rnot in seedflexiv-rizon4not in seedassetsnot in seedapptronik-apollonot in seedfourier-n1not in seed
Compatible environments
0No environments list fishing-for-best-returns yet.
Datasets that reference this policy
0No datasets reference fishing-for-best-returns yet.