In the algorithm description of the paper (page 22), $s_\text{cur}$ is globally initialized only once and updated exclusively when the score improves. This causes $s_\text{cur}$ to increase monotonically and always equal $s_\text{best}$. Is this an intentional design choice?
Algorithm 1 SkillOpt skill optimization
1: scur ← s0, sbest ← s0, C ← ∅, B ← [ ], mmeta ← ∅
...
20: if scorecand > scorecur then
21: scur ← s˜; scorecur ← scorecand
22: if scorecand > scorebest then
23: sbest ← s˜; scorebest ← scorecand
24: end if
...
In the algorithm description of the paper (page 22),$s_\text{cur}$ is globally initialized only once and updated exclusively when the score improves. This causes $s_\text{cur}$ to increase monotonically and always equal $s_\text{best}$ . Is this an intentional design choice?
Algorithm 1 SkillOpt skill optimization
1: scur ← s0, sbest ← s0, C ← ∅, B ← [ ], mmeta ← ∅
...
20: if scorecand > scorecur then
21: scur ← s˜; scorecur ← scorecand
22: if scorecand > scorebest then
23: sbest ← s˜; scorebest ← scorecand
24: end if
...