  We use the Deep Q-Network with reinforcement learning to investigate the
emergence of odd elasticity in an elastic microswimmer model. For an elastic
microswimmer, it is challenging to obtain the optimized dynamics due to the
intricate elastohydrodynamic interactions. However, our machine-trained model
adopts a novel transition strategy (the waiting behavior) to optimize the
locomotion. For the trained microswimmers, we evaluate the performance of the
cycles by the product of the loop area (called non-reciprocality) and the loop
frequency, and show that the average swimming velocity is proportional to the
performance. By calculating the force-displacement correlations, we obtain the
effective odd elasticity of the microswimmer to characterize its non-reciprocal
dynamics. This emergent odd elasticity is shown to be closely related to the
loop frequency of the cyclic deformation. Our work demonstrates the utility of
machine learning in achieving optimal dynamics for elastic microswimmers and
introduces post-analysis methods to extract crucial physical quantities such as
non-reciprocality and odd elasticity.
