Machine Learning, Optimization, Gradient Descent, Reinforcement Learning.
is actually a deterministic policy (stochastic policies are only optimal in adversarial and imperfect information settings). For example if t<0.5, then the pay-off is maximized if we always pick b=1 since each time we pick b=1 the reward is higher than choosing b=0. Therefore the solution is θ=1.