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blackbox-backprop

Torch modules that wrap blackbox combinatorial solvers according to the method presented in "Differentiating Blackbox Combinatorial Solvers"

dc3

DC3: A Learning Method for Optimization with Hard Constraints

dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

fjsp-drl

giril

ICML'20: Intrinsic Reward Driven Imitation Learning via Generative Model

gnnpapers

Must-read papers on graph neural networks (GNN)

hybrid-cp-rl-solver

Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

iclr2021-openreviewdata

Crawl & visualize ICLR papers and reviews.

jax_verify

Neural network verification in JAX

learn2branch

Exact Combinatorial Optimization with Graph Convolutional Neural Networks (NeurIPS 2019)

learntocut

Code implementation for NeurIPS 2019 submission 'Reinforcement Learning for Integer Programming: Learning to Cut'

milp_constrained_gan

Using MILP to encode constraints of generated game levels.

pdp-solver

PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers

pglib-uc

Benchmarks for the Unit Commitment Problem

pyscipopt

Python interface for the SCIP Optimization Suite

pytorch-a2c-ppo-acktr-gail

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

rlpyt

Reinforcement Learning in PyTorch

wenbo11.github.io

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