  De novo genome assembly is a relevant but computationally complex task in
genomics. Although de novo assemblers have been used successfully in several
genomics projects, there is still no 'best assembler', and the choice and setup
of assemblers still rely on bioinformatics experts. Thus, as with other
computationally complex problems, machine learning may emerge as an alternative
(or complementary) way for developing more accurate and automated assemblers.
Reinforcement learning has proven promising for solving complex activities
without supervision - such games - and there is a pressing need to understand
the limits of this approach to 'real' problems, such as the DFA problem. This
study aimed to shed light on the application of machine learning, using
reinforcement learning (RL), in genome assembly. We expanded upon the sole
previous approach found in the literature to solve this problem by carefully
exploring the learning aspects of the proposed intelligent agent, which uses
the Q-learning algorithm, and we provided insights for the next steps of
automated genome assembly development. We improved the reward system and
optimized the exploration of the state space based on pruning and in
collaboration with evolutionary computing. We tested the new approaches on 23
new larger environments, which are all available on the internet. Our results
suggest consistent performance progress; however, we also found limitations,
especially concerning the high dimensionality of state and action spaces.
Finally, we discuss paths for achieving efficient and automated genome assembly
in real scenarios considering successful RL applications - including deep
reinforcement learning.
