policy
Reinforcement-learning
forough90 · PyTorch
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Overview
Name
Reinforcement-learning
Author
forough90
Framework
PyTorch
License
unknown
Skill type
other
Evidence level
untested
Task description
This repository contains an agent-based labor-market simulation where a TD3-trained reinforcement-learning firm competes against a rule-based, trend-following firm. It includes reproducible training scripts, environment setup instructions, and analysis of the resulting dynamics and policies.
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 Reinforcement-learning yet.
Datasets that reference this policy
0No datasets reference Reinforcement-learning yet.