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Exploration via Sample-Efficient Subgoal Design

What do we have

Requirements

  • 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.

Algorithms

  • BESD
  • EI
  • LCB
  • HyperBand

How to run the algorithms

run BESD

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

This is the main file for running BESD. It requests 5 inputs:

  1. The environment: miso_gw, miso_ky, miso_it, miso_mc

  2. Which_problem: 0

  3. Version: 0

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

  5. Problem name:

    miso_gw: gw10Two2, gw20Three1.

    miso_ky: ky10One.

    miso_it: it10.

    miso_mc: mcf2.

EI / LCB

(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,...

HyperBand

(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,...

Vanilla Q-learning

(VIRT_ENV) $ python main_ql.py it10 0
  1. Problem name: gw10Two1, gw20Three1, ky10One, it10, mcf2

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

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