policy
Network-Anomaly-Detection-using-RL-model-and-Autoencoders
kumarpiyushraj · PyTorch
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Overview
Name
Network-Anomaly-Detection-using-RL-model-and-Autoencoders
Author
kumarpiyushraj
Framework
PyTorch
License
unknown
Skill type
other
Evidence level
untested
Task description
This project presents an integrated anomaly detection framework combining Autoencoders and Proximal Policy Optimization (PPO) reinforcement learning. Three types of Autoencoders—FeedForward, Denoising, and Convolutional—are used for feature extraction and reconstruction error analysis. Each model is
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 Network-Anomaly-Detection-using-RL-model-and-Autoencoders yet.
Datasets that reference this policy
0No datasets reference Network-Anomaly-Detection-using-RL-model-and-Autoencoders yet.