Data from: Smart self-propelled particles: A framework to investigate the cognitive bases of movement

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)