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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>
=== Learning ===