update.

README.md

@@ -6,7 +6,7 @@ Reinforcement learning (RL) has become an important training paradigm for large
 \max_{\pi} \; \mathbb{E}_{\tau \sim \pi}
 \max_{\pi} \; \mathbb{E}_{\tau \sim \pi}
 \left[
 \left[
 \sum_{t=0}^{T} r_t
 \sum_{t=0}^{T} r_t
-\right],
+\right]
 ````
 ````
 
 
 where $\pi$ denotes the policy, $\tau$ denotes a trajectory of interactions, and $r_t$ is the reward received at step $t$.
 where $\pi$ denotes the policy, $\tau$ denotes a trajectory of interactions, and $r_t$ is the reward received at step $t$.