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Simplify Skill implementation

Currently we handle the UCI_Elo with a double randomization. This
seems not necessary and a bit involuted.

This patch removes the first randomization and unifies the 2 cases.

closes https://github.com/official-stockfish/Stockfish/pull/3769

No functional change.
pull/3764/head^2
xefoci7612 2021-10-10 14:03:51 +02:00 committed by Joost VandeVondele
parent 0e89d6e754
commit ef4822aa8d
2 changed files with 20 additions and 22 deletions

View File

@ -194,6 +194,7 @@ tttak
Unai Corzo (unaiic)
Uri Blass (uriblass)
Vince Negri (cuddlestmonkey)
xefoci7612
zz4032

View File

@ -112,14 +112,22 @@ namespace {
return thisThread->state;
}
// Skill structure is used to implement strength limit
// Skill structure is used to implement strength limit. If we have an uci_elo then
// we convert it to a suitable fractional skill level using anchoring to CCRL Elo
// (goldfish 1.13 = 2000) and a fit through Ordo derived Elo for match (TC 60+0.6)
// results spanning a wide range of k values.
struct Skill {
explicit Skill(int l) : level(l) {}
bool enabled() const { return level < 20; }
bool time_to_pick(Depth depth) const { return depth == 1 + level; }
Skill(int skill_level, int uci_elo) {
if (uci_elo)
level = std::clamp(std::pow((uci_elo - 1346.6) / 143.4, 1 / 0.806), 0.0, 20.0);
else
level = double(skill_level);
}
bool enabled() const { return level < 20.0; }
bool time_to_pick(Depth depth) const { return depth == 1 + int(level); }
Move pick_best(size_t multiPV);
int level;
double level;
Move best = MOVE_NONE;
};
@ -243,10 +251,11 @@ void MainThread::search() {
Time.availableNodes += Limits.inc[us] - Threads.nodes_searched();
Thread* bestThread = this;
Skill skill = Skill(Options["Skill Level"], Options["UCI_LimitStrength"] ? int(Options["UCI_Elo"]) : 0);
if ( int(Options["MultiPV"]) == 1
&& !Limits.depth
&& !(Skill(Options["Skill Level"]).enabled() || int(Options["UCI_LimitStrength"]))
&& !skill.enabled()
&& rootMoves[0].pv[0] != MOVE_NONE)
bestThread = Threads.get_best_thread();
@ -311,19 +320,7 @@ void Thread::search() {
std::fill(&lowPlyHistory[MAX_LPH - 2][0], &lowPlyHistory.back().back() + 1, 0);
size_t multiPV = size_t(Options["MultiPV"]);
// Pick integer skill levels, but non-deterministically round up or down
// such that the average integer skill corresponds to the input floating point one.
// UCI_Elo is converted to a suitable fractional skill level, using anchoring
// to CCRL Elo (goldfish 1.13 = 2000) and a fit through Ordo derived Elo
// for match (TC 60+0.6) results spanning a wide range of k values.
PRNG rng(now());
double floatLevel = Options["UCI_LimitStrength"] ?
std::clamp(std::pow((Options["UCI_Elo"] - 1346.6) / 143.4, 1 / 0.806), 0.0, 20.0) :
double(Options["Skill Level"]);
int intLevel = int(floatLevel) +
((floatLevel - int(floatLevel)) * 1024 > rng.rand<unsigned>() % 1024 ? 1 : 0);
Skill skill(intLevel);
Skill skill(Options["Skill Level"], Options["UCI_LimitStrength"] ? int(Options["UCI_Elo"]) : 0);
// When playing with strength handicap enable MultiPV search that we will
// use behind the scenes to retrieve a set of possible moves.
@ -1780,8 +1777,8 @@ moves_loop: // When in check, search starts here
// RootMoves are already sorted by score in descending order
Value topScore = rootMoves[0].score;
int delta = std::min(topScore - rootMoves[multiPV - 1].score, PawnValueMg);
int weakness = 120 - 2 * level;
int maxScore = -VALUE_INFINITE;
double weakness = 120 - 2 * level;
// Choose best move. For each move score we add two terms, both dependent on
// weakness. One is deterministic and bigger for weaker levels, and one is
@ -1789,8 +1786,8 @@ moves_loop: // When in check, search starts here
for (size_t i = 0; i < multiPV; ++i)
{
// This is our magic formula
int push = ( weakness * int(topScore - rootMoves[i].score)
+ delta * (rng.rand<unsigned>() % weakness)) / 128;
int push = int(( weakness * int(topScore - rootMoves[i].score)
+ delta * (rng.rand<unsigned>() % int(weakness))) / 128);
if (rootMoves[i].score + push >= maxScore)
{