Algorithm 2 Student Policy OpTimization (SPOT)

1:Input: ${\mathcal{D}}_B$ and ${\mathcal{D}}_E$                                           Stage 01 2:Output: ${\tau }_{\theta }$ 3:$f_{\varphi }\left(\tau \right|\mathcal{X})\leftarrow {\mathcal{D}}_B$                                                   Stage 02 as per Equation (1) 4:Initialize ${\pi }_{\theta }$, ${\mathcal{D}}_{\pi }$, $L_E$ 5:while not done do: 6:      Episode = sample(${\mathcal{D}}_E$) 7:      for $i=0:L_E$ do: 8:             $(\mathrm{state}:x_{t_c},\mathrm{action}:f_{\varphi }\left(x_d\right))\leftarrow f_{\varphi }\left({\tau }_{\varphi }\right|x_d)$ 9:             Append: ${\mathcal{D}}_i\leftarrow \langle x_{t_c},{\tau }_{\varphi }\rangle$ 10:           Policy Buffer: ${\mathcal{D}}_{\pi }\leftarrow {\mathcal{D}}_{\pi }\cup {\mathcal{D}}_i$ 11:           Every $n_b$   and   ${\mathcal{D}}_{\pi }\ge 2L_E$ do: 12:                  Compute Loss: ${\mathcal{L}}_{\theta }$                         From Equation (2) 13:                  Policy Training: ${\pi }_{\theta }\leftarrow {\mathcal{L}}_{\theta }$          Stage 03