For combinatorial optimizations (discrete variables) and nonconvex optimizations (continuous variables), we aim to find high-quality local optimum, or even global optimum.

For combinatorial optimization problems, we compare with Benchmark.

This project is built based on ElegantRL and OpenAI Gym.

The following two key technologies are under active development:

• Massively parallel simuations of gym-environments on GPU, using thousands of CUDA cores and tensor cores.

• Podracer scheduling on a GPU cloud, e.g., DGX-2 SuperPod.

Several key references:

• Mazyavkina, Nina, et al. "Reinforcement learning for combinatorial optimization: A survey." Computers & Operations Research 134 (2021): 105400.

• Bengio, Yoshua, Andrea Lodi, and Antoine Prouvost. "Machine learning for combinatorial optimization: a methodological tour d’horizon." European Journal of Operational Research 290.2 (2021): 405-421.

• Makoviychuk, Viktor, et al. "Isaac Gym: High Performance GPU Based Physics Simulation For Robot Learning." Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). 2021.

• Nair, Vinod, et al. "Solving mixed integer programs using neural networks." arXiv preprint arXiv:2012.13349 (2020).

• File Structure

RLSolver
├── optimal
|   ├──branch-and-bound.py
|   └──cutting_plane.py
├── helloworld
|   ├──milp
|   ├──tsp
|   └──graph_maxcut
└── rlsolver (main folder)
    ├── envs
    |   (nonconvex optimizations)
    |   ├── learn2optimize
    |   └── mimo_beamforming 
    |   (combinatorial optimizations)
    |   ├── portfolio_management
    |   ├── quantum_circuits
    |   ├── vehicle_routing
    |   ├── virtual_machine_placement
    |   └── chip_design
    |── rlsolver_learn2optimize
    |── rlsolver_mimo_beamforming
    |── rlsolver_portfolio_management
    |── rlsolver_quantum_circuits
    └── utils

• mimo_beamforming
• graph_maxcut
• traveling salesman problem
• portfolio_management