1. Knowledge State Encoder Knowledge state vector is generated as: $h_t = RNN(x_t)$ $KS_t = W_{h, KS} \cdot h_t + b_{h, KS}$

LSTMs and GRUs were used as RNN variants.

2. Skill Encoder $e_{t+1}$ is embedded to $s_{t+1}$ as: $o_{t+1} = ReLU (W_1 \cdot (ReLU(W_0 \cdot e_{t+1} + b_0 )) + b_1)$ $s_{t+1} = L2-normalize(o_{t+1})$ $s_{t+1} \in U ^d = { v\in \R^d : ||v|| = 1, v^i \ge 0 \quad i = 1, ... , d}$