Soft Robot evolution: Evolution Gym and Map Elites

Soft robot evolution using:

• EvolutionGym benchmark, to co-optimize control and design and run simulations
• Map Elites, provided by QDPY, to promote diversity

Installation

Clone the repository:

git clone https://github.com/elisacomposta/soft-robot-evolution.git

Install qdpy:

pip3 install qdpy[all]

Inside the main folder, clone EvolutionGym repository:

git clone --recurse-submodules https://github.com/elisacomposta/evogym.git

and follow the instructions to install it at evogym repository

Run example

Parameters can be defined in run_qd.py:

• experiment_name = name of the experiment; all results are saved in results/experiment_name
• configFileName = name of the YAML configuration file
• num_cores = number of robots to train in parallel
• parallelismType = type of parallelism used for the illumination process.
  Note: QDPY proposes many types of executors ('sequential', 'multithreading', 'multiprocessing', 'concurrent', 'scoop', 'ray'). For more details see qdpy/base.py.

The environment name and other parameters can be defined in the configuration file. See the examples in conf/.

Run the example using the following command.

python run_qd.py --algo ppo --use-gae --lr 2.5e-4 --clip-param 0.1 --value-loss-coef 0.5 --num-processes 4 --num-steps 128 --num-mini-batch 4 --log-interval 100 --use-linear-lr-decay --entropy-coef 0.01 --no-cuda --eval-interval 20

All PPO hyperparameters are specified through command line arguments. For more details please see this repo.

Plot

avg_plots.py

It allows the realization of single or mediated maps and trends after an experiment has finished, thanks to the stored plot data.
Set the parameters:

• qd_plot, evogym_plot, compare_exp: one of them shoud be True, to choose which plot to make
• experiments: names of the stored experiments
• results_dir: path where to save the plots
• fitness_domain: set the domain of the fitness (optional)
• features: features used in the experiment
• tot_random: number of randomly generated individuals, to plot vertical line (optionl)

render_body.py

Store images of the evaluated individuals. Set the following parameters:

• exp_name: name of the experiment
• gen_algo: if true, looks for the experiment in evogym/examples/saved_data
• inds: list of labels of the individual to store
• store_in_order: if true, store design as num_indLabel
• generation: if using gen algo experiment

Visualize simulation

Run python visualize.py and follow the instructions to visualize the simulation an individual from a stored experiment.