amar-iastate

L2RPN-using-A3C

Reinforcement Learning using the Actor-Critic framework for the L2RPN challenge (https://l2rpn.chalearn.org/ & https://competitions.codalab.org/competitions/22845#learn_the_details-overview). The agent trained using this code was one of the winners of the challenge. The code runs on the pypownet environment (https://github.com/MarvinLer/pypownet). It is released under a license of LGPLv3

PMAPS2018

Mathematica code for PMAPS paper

pygcn

Graph Convolutional Networks in PyTorch

pypownet

A Power Network simulator, compatible with OpenAI Gym with a Reinforcement Learning-focused environment.

resume

sparse-evolutionary-artificial-neural-networks

sparse neural networks before training, sparse evolutionary artificial neural networks, scalable deep learning, very high dimensional data, complex networks

pygcn

Graph Convolutional Networks in PyTorch

amar-iastate/L2RPN-using-A3C

Reinforcement Learning using the Actor-Critic framework for the L2RPN challenge (https://l2rpn.chalearn.org/ & https://competitions.codalab.org/competitions/22845#learn_the_details-overview). The agent trained using this code was one of the winners of the challenge. The code runs on the pypownet environment (https://github.com/MarvinLer/pypownet). It is released under a license of LGPLv3

amar-iastate/sparse-evolutionary-artificial-neural-networks

sparse neural networks before training, sparse evolutionary artificial neural networks, scalable deep learning, very high dimensional data, complex networks

amar-iastate/PMAPS2018

Mathematica code for PMAPS paper

amar-iastate/pygcn

Graph Convolutional Networks in PyTorch

amar-iastate/pypownet

A Power Network simulator, compatible with OpenAI Gym with a Reinforcement Learning-focused environment.

amar-iastate/resume