Train an agent to play VizDoom with multi sensory inputs. Trained using sample factory
Paper: https://arxiv.org/abs/2107.02195
Website: https://sites.google.com/view/sound-rl
Tested on Ubuntu 18.04 64-bit.
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Install miniconda for Python 3.7: https://docs.conda.io/en/latest/miniconda.html
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Clone the repo:
git clone https://github.com/hegde95/Agents_that_Listen.git -
Create and activate conda env:
cd Agents_that_Listen
conda env create -f environment.yml
cd ..
conda activate sound
This will install sample-factory into the environment too
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Clone the required ViZDoom repo that contains the sound state space:
git clone https://github.com/hegde95/ViZDoom_with_Sound.git -
Build and install the new environemnt:
cd ViZDoom_with_Sound
python setup.py build && python setup.py install
cd ..
- Enter the ViZDoom_Sound folder and run the following command to start a training run on the instruction scenario discussed in the paper
cd Agents_that_Listen
python -m rl.train --algo=APPO --env=doomsound_instruction --experiment=doom_instruction --encoder_custom=vizdoomSoundFFT --train_for_env_steps=500000000 --num_workers=24 --num_envs_per_worker=20
- To view a rollout of the learned policy run the following line:
python -m rl.enjoy --algo=APPO --env=doomsound_instruction --experiment=doom_instruction
- To record a demo of the learned policy run the following command:
python -m demo.enjoy_appo --algo=APPO --env=doomsound_instruction --experiment=doom_instruction
@inproceedings{hegde2021agents,
title={Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory Systems},
author={Hegde, Shashank and Kanervisto, Anssi and Petrenko, Aleksei},
booktitle={To Appear in IEEE COG 2021},
year={2021}
}