  Traditional memory writing operations proceed one bit at a time, where e.g.
an individual magnetic domain is force-flipped by a localized external field.
One way to increase material storage capacity would be to write several bits at
a time in the bulk of the material. However, the manipulation of bits is
commonly done through quasi-static operations. While simple to model, this
method is known to reduce memory capacity. In this paper, we demonstrate how a
reinforcement learning agent can exploit the dynamical response of a simple
multi-bit mechanical system to restore its memory to full capacity. To do so,
we introduce a model framework consisting of a chain of bi-stable springs,
which is manipulated on one end by the external action of the agent. We show
that the agent manages to learn how to reach all available states for three
springs, even though some states are not reachable through adiabatic
manipulation, and that both the training speed and convergence within physical
parameter space are improved using transfer learning techniques. Interestingly,
the agent also points to an optimal design of the system in terms of writing
time. In fact, it appears to learn how to take advantage of the underlying
physics: the control time exhibits a non-monotonic dependence on the internal
dissipation, reaching a minimum at a cross-over shown to verify a mechanically
motivated scaling relation.
