  The use of reinforcement learning has proven to be very promising for solving
complex activities without human supervision during their learning process.
However, their successful applications are predominantly focused on fictional
and entertainment problems - such as games. Based on the above, this work aims
to shed light on the application of reinforcement learning to solve this
relevant real-world problem, the genome assembly. By expanding the only
approach found in the literature that addresses this problem, we carefully
explored the aspects of intelligent agent learning, performed by the Q-learning
algorithm, to understand its suitability to be applied in scenarios whose
characteristics are more similar to those faced by real genome projects. The
improvements proposed here include changing the previously proposed reward
system and including state space exploration optimization strategies based on
dynamic pruning and mutual collaboration with evolutionary computing. These
investigations were tried on 23 new environments with larger inputs than those
used previously. All these environments are freely available on the internet
for the evolution of this research by the scientific community. The results
suggest consistent performance progress using the proposed improvements,
however, they also demonstrate the limitations of them, especially related to
the high dimensionality of state and action spaces. We also present, later, the
paths that can be traced to tackle genome assembly efficiently in real
scenarios considering recent, successfully reinforcement learning applications
- including deep reinforcement learning - from other domains dealing with
high-dimensional inputs.
