  A study of the effect of thermal dissipation on quantum reinforcement
learning is performed. For this purpose, a nondissipative quantum reinforcement
learning protocol is adapted to the presence of thermal dissipation. Analytical
calculations as well as numerical simulations are carried out obtaining
evidence that dissipation do not significantly degrade the performance of the
quantum reinforcement learning protocol for sufficiently low temperatures,
being in some cases even beneficial. Quantum reinforcement learning under
realistic experimental conditions of thermal dissipation opens an avenue for
the realization of quantum agents able to interact with a changing environment,
and adapt to it, with plausible many applications inside quantum technologies
and machine learning.
