  A combination of deep reinforcement learning and supervised learning is
proposed for the problem of active sequential hypothesis testing in completely
unknown environments. We make no assumptions about the prior probability, the
action and observation sets, and the observation generating process. Our method
can be used in any environment even if it has continuous observations or
actions, and performs competitively and sometimes better than the Chernoff
test, in both finite and infinite horizon problems, despite not having access
to the environment dynamics.
