  Behavioral experiments on the trust game have shown that trust and
trustworthiness are universal among human beings, contradicting the prediction
by assuming \emph{Homo economicus} in orthodox Economics. This means some
mechanism must be at work that favors their emergence. Most previous
explanations however need to resort to some factors based upon imitative
learning, a simple version of social learning. Here, we turn to the paradigm of
reinforcement learning, where individuals update their strategies by evaluating
the long-term return through accumulated experience. Specifically, we
investigate the trust game with the Q-learning algorithm, where each
participant is associated with two evolving Q-tables that guide one's decision
making as trustor and trustee respectively. In the pairwise scenario, we reveal
that high levels of trust and trustworthiness emerge when individuals
appreciate both their historical experience and returns in the future.
Mechanistically, the evolution of the Q-tables shows a crossover that resembles
human's psychological changes. We also provide the phase diagram for the game
parameters, where the boundary analysis is conducted. These findings are robust
when the scenario is extended to a latticed population. Our results thus
provide a natural explanation for the emergence of trust and trustworthiness
without external factors involved. More importantly, the proposed paradigm
shows the potential in deciphering many puzzles in human behaviors.
