  Reinforcement learning synthesizes controllers without prior knowledge of the
system. At each timestep, a reward is given. The controllers optimize the
discounted sum of these rewards. Applying this class of algorithms requires
designing a reward scheme, which is typically done manually. The designer must
ensure that their intent is accurately captured. This may not be trivial, and
is prone to error. An alternative to this manual programming, akin to
programming directly in assembly, is to specify the objective in a formal
language and have it "compiled" to a reward scheme. Mungojerrie
(https://plv.colorado.edu/mungojerrie/) is a tool for testing reward schemes
for $\omega$-regular objectives on finite models. The tool contains
reinforcement learning algorithms and a probabilistic model checker.
Mungojerrie supports models specified in PRISM and $\omega$-automata specified
in HOA.
