  In the last few years, the research activity around reinforcement learning
tasks formulated over environments with sparse rewards has been especially
notable. Among the numerous approaches proposed to deal with these hard
exploration problems, intrinsic motivation mechanisms are arguably among the
most studied alternatives to date. Advances reported in this area over time
have tackled the exploration issue by proposing new algorithmic ideas to
generate alternative mechanisms to measure the novelty. However, most efforts
in this direction have overlooked the influence of different design choices and
parameter settings that have also been introduced to improve the effect of the
generated intrinsic bonus, forgetting the application of those choices to other
intrinsic motivation techniques that may also benefit of them. Furthermore,
some of those intrinsic methods are applied with different base reinforcement
algorithms (e.g. PPO, IMPALA) and neural network architectures, being hard to
fairly compare the provided results and the actual progress provided by each
solution. The goal of this work is to stress on this crucial matter in
reinforcement learning over hard exploration environments, exposing the
variability and susceptibility of avant-garde intrinsic motivation techniques
to diverse design factors. Ultimately, our experiments herein reported
underscore the importance of a careful selection of these design aspects
coupled with the exploration requirements of the environment and the task in
question under the same setup, so that fair comparisons can be guaranteed.
