policy
Intelligent-Quadruped-Robot-Locomotion-using-Proximal-Policy-Optimization-Agent.
KorukondaAnusha · PyTorch
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Overview
Name
Intelligent-Quadruped-Robot-Locomotion-using-Proximal-Policy-Optimization-Agent.
Author
KorukondaAnusha
Framework
PyTorch
License
unknown
Skill type
locomotion
Evidence level
untested
Task description
Implemented Proximal Policy Optimization (PPO) in Reinforcement Learning using MATLAB to enable stable and efficient movement of a quadruped robot on complex terrains. The approach improves the robot’s control by balancing learning and performance, enhancing stability and adaptability for applicatio
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 Intelligent-Quadruped-Robot-Locomotion-using-Proximal-Policy-Optimization-Agent. yet.
Datasets that reference this policy
0No datasets reference Intelligent-Quadruped-Robot-Locomotion-using-Proximal-Policy-Optimization-Agent. yet.