One sentence summary: ElegantRL_Solver is a high-performance RL Solver.

We aim to find high-quality optimum, or even (nearly) global optimum, for nonconvex/nonlinear optimizations (continuous variables) and combinatorial optimizations (discrete variables). We provide pretrained neural networks to perform real-time inference for nonconvex optimization problems, including combinatorial optimization problems.

This project is built on [ElegantRL](https://github.com/AI4Finance-Foundation/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.

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).

  • MIMO Beamforming in 5G/6G.

  • Classical NP-Hard problems.

  • Classical Simulation of Quantum Circuits.

  • Compressive Sensing.

  • Portfolio Management.

  • OR-Gym.

File Structure: ` -RLSolver -├── opt_methods -|   ├──branch-and-bound.py -|   └──cutting_plane.py -├── helloworld -|   ├──maxcut.py -|   ├──maxcut_env.py -└── rlsolver (main folder) -    ├── envs -    |   ├── _base -    |   └── maxcut -    |   └── tsp -    |   ├── portfolio_management -    |── rlsolver_learn2opt -    |   ├── mimo -    |   ├── tensor_train -    └── utils -    └── graph_partitioning.py -    └── graph_partitioning_gurobi.py -    └── maxcut.py -    └── maxcut_gurobi.py -    └── tsp.py -    └── tsp_gurobi.py `

ElegantRL_Solver features high-performance and stability:

High-performance: it can find high-quality optimum, or even (nearly) global optimum.

Stable: it leverages computing resource to implement the Hamiltonian-term as an add-on regularization to DRL algorithms. Such an add-on H-term utilizes computing power (can be computed in parallel on GPU) to search for the “minimum-energy state”, corresponding to the stable state of a system.