Automatic Noise Filtering

with Dynamic Sparse Training in Deep Reinforcement Learning

Paper: arxiv.org/abs/2302.06548 accepted at AAMAS'23. If you use this code, please cite:

@article{grooten2023automatic,
    title={{Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning}}, 
    author={Grooten, Bram and Sokar, Ghada and Dohare, Shibhansh and Mocanu, Elena and Taylor, Matthew E. and Pechenizkiy, Mykola and Mocanu, Decebal Constantin},
    year={2023},
    journal={The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
    note={URL: \url{https://arxiv.org/abs/2302.06548}}
}

Abstract

Tomorrow's robots will need to distinguish useful information from noise when performing different tasks. A household robot for instance may continuously receive a plethora of information about the home, but needs to focus on just a small subset to successfully execute its current chore.

Filtering distracting inputs that contain irrelevant data has received little attention in the reinforcement learning literature. To start resolving this, we formulate a problem setting in reinforcement learning called the extremely noisy environment (ENE) where up to 99% of the input features are pure noise. Agents need to detect which features actually provide task-relevant information about the state of the environment.

Consequently, we propose a new method termed Automatic Noise Filtering (ANF) which uses the principles of dynamic sparse training to focus the input layer's connectivity on task-relevant features. ANF outperforms standard SAC and TD3 by a large margin, while using up to 95% fewer weights.

Furthermore, we devise a transfer learning setting for ENEs, by permuting all features of the environment after 1M timesteps to simulate the fact that other information sources can become task-relevant as the world evolves. Again ANF surpasses the baselines in final performance and sample complexity.

Install

Requirements

• Python 3.8
• PyTorch 1.9
• MuJoCo-py
• OpenAI gym
• Linux (using WSL may work in Windows)

Instructions

First make a virtual environment:

sudo apt install python3.8 python3.8-venv python3.8-dev
python3.8 -m venv venv
source venv/bin/activate

If you don't have MuJoCo 2.10 yet:

cd ~
wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz
tar -xzf mujoco210-linux-x86_64.tar.gz
mkdir .mujoco
mv mujoco210 .mujoco/
rm mujoco210-linux-x86_64.tar.gz

Now you have MuJoCo. Proceed with:

pip install mujoco_py==2.1.2.14 gym==0.21.0 torch==1.9.0
pip install wandb --upgrade

Now try to import mujoco_py in a python console, and do what the error messages tell you. (Like adding lines to your .bashrc file.)

$ python
>>> import mujoco_py

You may need to install the following packages to solve some errors:

sudo apt install libosmesa6-dev libglew-dev patchelf

Usage

Train

To train an ANF agent on the ENE with 90% noise features, run:

python main.py \
    --policy ANF-SAC \
    --env HalfCheetah-v3 \
    --fake_features 0.9 \
    --input_layer_sparsity 0.8 \
    --wandb_mode disabled

Possible policies: ANF-SAC, ANF-TD3, SAC, TD3.

Possible environments: HalfCheetah-v3, Hopper-v3, Walker2d-v3, Humanoid-v3.

Show all available arguments: python main.py --help

Test

See the file view_mujoco.py to test a trained agent on a single episode and view its behavior.