Recall that the objective of Reinforcement Learning is to find an optimal policy <math>\pi^*<\math> which we encode in a neural network with parameters <math>\theta^*<\/math>. These optimal parameters are defined as<math>\theta^* = \text<argmax>_\theta E_{\tau \sim p_\theta(\tau)} \left[ \sum_t r(s_t, a_t) \right] <\/math>