  Discrete-choice life cycle models of labor supply can be used to estimate how
social security reforms influence employment rate. In a life cycle model,
optimal employment choices during the life course of an individual must be
solved. Mostly, life cycle models have been solved with dynamic programming,
which is not feasible when the state space is large, as often is the case in a
realistic life cycle model. Solving a complex life cycle model requires the use
of approximate methods, such as reinforced learning algorithms. We compare how
well a deep reinforced learning algorithm ACKTR and dynamic programming solve a
relatively simple life cycle model. To analyze results, we use a selection of
statistics and also compare the resulting optimal employment choices at various
states. The statistics demonstrate that ACKTR yields almost as good results as
dynamic programming. Qualitatively, dynamic programming yields more spiked
aggregate employment profiles than ACKTR. The results obtained with ACKTR
provide a good, yet not perfect, approximation to the results of dynamic
programming. In addition to the baseline case, we analyze two social security
reforms: (1) an increase of retirement age, and (2) universal basic income. Our
results suggest that reinforced learning algorithms can be of significant value
in developing social security reforms.
