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MaximilienLC / nra

Neuroevolution of Recurrent Architectures on Control Tasks
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Neuroevolution of Recurrent Architectures on Control Tasks

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This repository contains the instructions on how to reproduce the experiments from the paper "Neuroevolution of Recurrent Architectures on Control Tasks" published at GECCO 2022 & the ALOE ICLR 2022 Workshop.

The code itself is located in a larger library called Nevo.

Installation (tested on Ubuntu 20.04)

# Debian packages                       ----------mpi4py---------- ~~~~~~~~~~~~~~~~~Gym~~~~~~~~~~~~~~~~~~~
sudo apt install git python3-virtualenv python3-dev libopenmpi-dev g++ swig libosmesa6-dev patchelf ffmpeg

# MuJoCo
wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -P ~/Downloads/
mkdir -p ~/.mujoco/ && tar -zxf ~/Downloads/mujoco210-linux-x86_64.tar.gz -C ~/.mujoco/
echo -e "\n# MuJoCo\nMUJOCO_PATH=~/.mujoco/mujoco210/bin
export LD_LIBRARY_PATH=\$MUJOCO_PATH:\$LD_LIBRARY_PATH" >> ~/.bashrc
source ~/.bashrc

# Library & Dependencies
git clone https://github.com/MaximilienLC/nevo && cd nevo/
virtualenv venv && source venv/bin/activate && pip3 install -r requirements.txt

Execution

<task> = acrobot, cart_pole, mountain_car, mountain_car_continuous, pendulum, bipedal_walker, bipedal_walker_hardcore, lunar_lander, lunar_lander_continuous, ant, half_cheetah, hopper, humanoid, humanoid_standup, inverted_double_pendulum, inverted_pendulum, reacher, swimmer or walker_2d
<net> = static/rnn or dynamic/rnn 

mpiexec -n <n> python3 main.py --env_path envs/multistep/score/control.py \
                               --bots_path bots/network/<net>/control.py \
                               --nb_generations <nb_generations> \
                               --population_size <population_size> \
                               --additional_arguments '{"task" : "<task>"}'

Example from the paper: (you can increase the number of MPI processes if your machine allows it)

mpiexec -n 2 python3 main.py --env_path envs/multistep/score/control.py \
                             --bots_path bots/network/dynamic/rnn/control.py \
                             --nb_generations 100 \
                             --population_size 16 \
                             --additional_arguments '{"task" : "acrobot"}'

Downloading the Paper's Results & Final Dynamic States

wget https://www.dropbox.com/s/if66cy3ydep6xj0/envs.multistep.control.score.zip -P ~/Downloads/
unzip -o ~/Downloads/envs.multistep.control.score.zip -d data/states/

You can now run additional generations ...

mpiexec -n 4 python3 main.py --env_path envs/multistep/score/control.py \
                             --bots_path bots/network/dynamic/rnn/control.py \
                             --nb_elapsed_generations 100 \
                             --nb_generations 10 \
                             --population_size 16 \
                             --additional_arguments '{"task" : "cart_pole"}'

... Evaluate the new agents ...

mpiexec -n 4 python3 utils/evaluate.py --states_path data/states/envs.multistep.score.control/steps.0~task.cart_pole~transfer.no~trials.1/bots.network.dynamic.rnn.control/16/

... And both record the elite's behaviour and obtain its architecture.

python3 utils/record.py --state_path data/states/envs.multistep.score.control/steps.0~task.cart_pole~transfer.no~trials.1/bots.network.dynamic.rnn.control/16/110/

Reproducing the Paper's Figures

# Verify Stable Baselines 3 Results (not necessary for later steps)
git clone --recursive https://github.com/DLR-RM/rl-baselines3-zoo ~/rl-baselines3-zoo
jupyter notebook utils/notebooks/neuroevolution-recurrent-architectures/sb3_baselines.ipynb

# Visualize the dynamic networks
jupyter notebook utils/notebooks/neuroevolution-recurrent-architectures/dynamic_rnn.ipynb

# Reproduce the paper's figures  
jupyter notebook utils/notebooks/neuroevolution-recurrent-architectures/figures.ipynb