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Interferobot: aligning an optical interferometer by a reinforcement learning agent

This repository is the official implementation of Interferobot: aligning an optical interferometer by a reinforcement learning agent .

Structure

This repo contains the following four submodules:

  • interf_game - the interactive user interface for both simulator and hardware interferometer
  • gym_interf - gym environment for interferometer simulator
  • iron_interf - gym environment for hardware interferometer
  • interf_dqn - code to train double dueling dqn agent

Installation

Clone including submodules

git clone --recurse-submodules git@github.com:dmitrySorokin/interferobotProject.git

Install interferometer simulator

pip3 install -e gym_interf

Install user interface

pip3 install -r interf_game/requirements.txt

Install hardware interferometer

Install drivers and python bindings for mirror mounts

apt-get install libusb-1.0-0
pip3 install pyusb (1.0.2)

Install drivers from here and python bindings for IDS camera

pip3 install pyueye

Run camera daemon

./usr/bin/ueyeusbd

Install gym environment

pip3 install -e iron_interf

Pre-trained Models

Pretrained models located in interf_game submodule:

model description
interf_game/ablation_models/all_random model trained with all doman randomizations
interf_game/ablation_models/no_brightness_random model trained without brightness randomization
interf_game/ablation_models/no_channel_shift model trained without duty cycle randomization
interf_game/ablation_models/no_noise model trained without noise white nose
interf_game/ablation_models/no_radius_random model trained without radius randomization

Running

Interferometer simulator

Run simulator in iteractive mode:

cd interf_game
python3 main_sim.py --model=path/to/model

You can try to align the interferometer manually by keyboard:

  • w, a, s, d - controls mirror 1
  • i, j, k, l - controls beam splitter 2
  • r - reset the simulator to a random position
  • q - move to an aligned state
  • x - to change the step size

Or let the agent do this:

  • press space to run/stop the agent

Hardware interferometer

The same functionality is available for hardware interferometer in interf_game/main.py

Training

Train Interferobot with all domain randomizations:

cd interf_dqn
./run.sh

Evaluation

Evaluate the trained agent for 100 episodes:

cd interf_game
python3 eval_sim.py --model=ablation_models/all_random --ngames=100 --log_dir=all_random

This will create directory all_random with files:

  • log.txt - semicolon separated values igame, istep, visib_camera, visib_device
  • game_{igame}_step_{istep}.npz - numpy arxieves with fields state, action, next_state, done, visib_device, visib_camera

Visualize episoge 0:

./show.py --game_id=0 --folder=all_random

Experimental data

Evaluation data

Evaluation episodes collected at the physical interferometer for all trained models in the format described above are available at google drive.

Videos

Alignment videos generated for all_random model evaluated at the physical interferometer are available at google drive.

Results

Our models trained entirely in simulation achieves the following performance in zero-shot Sim2Real transfer:

Model name visibility return
all_random 0.96 ± 0.02 221 ± 54
no_radius_random 0.74 ± 0.20 85 ± 69
no_brightness_random 0.91 ± 0.04 178 ± 39
no_noise 0.82 ± 0.07 129 ± 43
no_channel_shift 0.89 ± 0.07 200 ± 42