• MOE
• Replace the corresponding files with the files in folder moe, and rerun moe/optimal_learning/cpp/CMakeLists.txt and moe/optimal_learning/cpp/CMakeCache.txt.

• BESD
• EI
• LCB
• HyperBand
• MAML
• Q-learning

(VIRT_ENV) $ python run_besd_REP.py miso_gw 0 0 gw10Two1

This is the main file for running BESD. It requests 4 inputs:

1. The environment: besd_gw, besd_ky, besd_it, besd_mc

2. Which problem: 0

3. Replication number: 0,1,2,...

4. Problem name:

   besd_gw: gw10Two2, gw20Three1.

   besd_ky: ky10One.

   besd_it: it10.

   besd_mc: mcf2.

(VIRT_ENV) $ python main_gpyopt.py gw10Two1 EI 0 0

1. Problem name: gw10Two1, gw20Three1, ky10One, it10, mcf2

2. Algorithm: EI, LCB

3. Version: 0

4. Replication_no: 0,1,2,...

(VIRT_ENV) $ python main_hb.py gw10Two1 0 0

1. Problem name: gw10Two1, gw20Three1, ky10One, it10, mcf2

2. Version: 0

3. Replication_no: 0,1,2,...

Go to folder maml-rl-pytorch, then run the following:

  (VIRT_ENV) $ python train.py --config configs/maml/gw10Two1.yaml --output-folder result_gw10Two1 --seed 0 --num-workers 8

(VIRT_ENV) $ python main_ql.py it10 0

1. Problem name: gw10Two1, gw20Three1, ky10One, it10, mcf2

2. Replication_no: 0,1,2,...