Update README.md

README.md

@@ -40,7 +40,7 @@ In other words, he is a reinforcement learning (RL) agent trained to compete at
 - Activation: `tanh`
 
 
-Mrs. Pong uses a shared Actor-Critic MLP designed for 2D table tennis control. The 16-dimensional observation vector is passed through two hidden layers of 192 units each, with Tanh activations between them. The resulting 192-dimensional representation is shared by both the actor and critic heads. The actor outputs categorical logits across the 3 movement actions (stay, up, down), while the critic uses the same representation to estimate the scalar state value V(s). This shared setup keeps the network small and fast on CPU while letting the model combine ball coordinates, velocities, paddle momentum, raycasted intercept points, and opponent court openings into a single control decision.
+Mr. Pong uses a shared Actor-Critic MLP designed for 2D table tennis control. The 16-dimensional observation vector is passed through two hidden layers of 192 units each, with Tanh activations between them. The resulting 192-dimensional representation is shared by both the actor and critic heads. The actor outputs categorical logits across the 3 movement actions (stay, up, down), while the critic uses the same representation to estimate the scalar state value V(s). This shared setup keeps the network small and fast on CPU while letting the model combine ball coordinates, velocities, paddle momentum, raycasted intercept points, and opponent court openings into a single control decision.
 
 ## Training Configuration