Operant　conditioning　is　one　of　the　fundamental　mechanisms　of　animal　learning,　which　suggests　that　the　behavior　of　all　animals,　from　protists　to　humans,　is　guided　by　its　consequences.　We　present　a　new　stochastic　learning　automaton　called　a　Skinner　automaton　that　is　a　psychological　model　for　formalizing　the　theory　of　operant　conditioning.　We　identify　animal　operant　learning　with　a　thermodynamic　process,　and　derive　a　so-called　Skinner　algorithm　from　Monte　Carlo　method　as　well　as　Metropolis　algorithm　and　simulated　annealing.　Under　certain　conditions,　we　prove　that　the　Skinner　automaton　is　expedient,　E　＞-optimal,　optimal,　and　that　the　operant　probabilities　converge　to　the　set　of　stable　roots　with　probability　of　1.　The　Skinner　automaton　enables　machines　to　autonomously　learn　in　an　animal-like　way.