  Reinforcement Learning offers a framework to learn to choose actions in order
to achieve some goal. However, at the nano-scale, thermal fluctuations hamper
the learning process. We analyze this regime using the general framework of
Markov Decision Processes, which applies to a wide variety of problems from
nano-navigation to nano-machine actuation. We show that at the nan-oscale,
while optimal actions should bring an improvement proportional to the small
ratio of the applied force times a length-scale over the temperature, the
learned improvement is smaller and proportional to the square of this small
ratio. Consequently, the efficiency of learning, which compares the learning
improvement to the theoretical optimal improvement, drops to zero.
Nevertheless, these limitations can be circumvented by using actions learned at
a lower temperature. These results are illustrated with simulations of the
control of the shape of small particle clusters.
