DOI: 10.5061/dryad.fttdz08z9
title: Data from: Smart self-propelled particles: A framework to investigate the cognitive bases of movement

description: We present a framework specifically developed to develop theories of
 spatial decision-making and to fully understand the rational of decisions
 embedded in an environment (and therefore the underlying evolutionary
 processes). This is achieved by the means of cognitive agents, moving
 thanks to artificial neural networks controlling movements and whose
 parameters are optimised with a genetic algorithms. Specifically, we
 investigate a simple task in which single agents need to learn to explore
 their square arena without leaving its boundaries. We show that agents
 evolve by developing increasingly optimal strategies to solve a
 spatially-embedded learning task while not having an initial arbitrary
 model of movements. The process allows the agents to learn how to move
 (i.e. by avoiding the arena walls) in order to make increasingly optimal
 decisions (improving their exploration of the arena). Our dataset is made
 of 4 sets of simulations: parameters of reference, a survival objective
 function, introducing a turning penalty and with a slower speed (see
 details of parameters below). Each set of simulations is made of 60 trials
 with different initial conditions, each of which has been simulated with
 20,000 agents for 150 generations. For each generation, we record: the
 score of the 20,000 agents across four runs with different and random
 initial conditions the score of the 20,000 agents from the same controlled
 initial condition (files with _com suffix) metadata with parameters used
 in this simulation parameters (weights and biases) of the artificial
 neural network of the best agent (i.e. with highest score) of each
 generation parameters (weights and biases) of the artificial neural
 network of the best agent (i.e. with highest score in the controlled
 initial condition run) of each generation (files with _com suffix)
description: The code generating this data is published as supplementary
 information of the article. The parameters
 corresponding to the sets of simulations presented are as
 follows: 
 Name Max. Turning
 Angle Nb of Neurons in HL
 Goal Speed Turning
 Penalty Parameters of
 reference 6.28 3
 Explore 50
 0 Survival objective
 function 6.28 3
 Survive 50
 0 Parameters with a
 turning penalty 6.28
 3 Explore
 50 0.33 
 Parameters with a slow speed
 6.28 3
 Explore 5 0
description: All files are text files with file extension .pkl. These files
 can be opened in Python and R using these two python functions to
 respectively read all simulation files and the files containing the
 parameters of the optimised artificial neural networks:
 import pickle def
 read_pickle_file(file):<br>    with open(file, "rb") as
 curfile:<br>        pickle_data = pickle.load(curfile)<br>   
 return pickle_data         def
 read_brain_file(file):<br>    with open(file, "rb") as
 curfile:<br>        pickle_data = pickle.load(curfile)<br>   
 return pickle_data.get_weights()     These functions
 can be called from Python or from R using the package Reticulate. For
 instance, these two lines in R will read the metadata file stored in the
 folder 'current.simulated.folder'
 library(reticulate) metadata &lt;-
 read_pickle_file(paste("current.simulation.folder/",
 "metadata.pkl", sep = ""))