WIP refactoring for aspiration window, minor other changes
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@ -127,7 +127,7 @@ proc storeHistoryScore(self: var SearchManager, sideToMove: PieceColor, move: Mo
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func isTactical(self: Move): bool =
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## Returns whether the given move
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## is a tactical move
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return self.isPromotion() or self.isCapture()
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return self.isPromotion() or self.isCapture() or self.isEnPassant()
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proc getEstimatedMoveScore(self: SearchManager, move: Move, ply: int): int =
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@ -170,7 +170,7 @@ proc getEstimatedMoveScore(self: SearchManager, move: Move, ply: int): int =
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return result + MVV_LVA_OFFSET
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if not move.isCapture():
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if not move.isCapture() and not move.isEnPassant():
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# History heuristic bonus
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return self.getHistoryScore(sideToMove, move) + HISTORY_OFFSET
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@ -421,7 +421,7 @@ proc search(self: var SearchManager, depth, ply: int, alpha, beta: Score, isPV:
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return
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bestScore = max(score, bestScore)
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if score >= beta:
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if not move.isCapture():
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if not move.isCapture() and not move.isEnPassant():
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# History heuristic: keep track of moves that caused a beta cutoff and order
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# them early in subsequent searches, as they might be really good later. A
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# quadratic bonus wrt. depth is usually the value that is used (though some
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@ -448,22 +448,45 @@ proc search(self: var SearchManager, depth, ply: int, alpha, beta: Score, isPV:
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break
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self.pvMoves[ply][i + 1] = pv
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self.pvMoves[ply][0] = move
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# TODO
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# else:
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# when defined(noScaleHistory):
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# if not move.isCapture() and self.history[][sideToMove][move.startSquare][move.targetSquare] > lowestEval():
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# # Here, we punish moves that failed to raise alpha. This allows us to avoid scaling our values
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# # after every search (which should retain more information about the explored subtreees) and
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# # makes sure that moves that we thought were good but aren't are pushed further in the move list
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# self.history[][sideToMove][move.startSquare][move.targetSquare] -= Score(depth * depth)
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# else:
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# discard
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let nodeType = if bestScore >= beta: LowerBound elif bestScore <= alpha: UpperBound else: Exact
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self.transpositionTable[].store(depth.uint8, bestScore, self.board.position.zobristKey, bestMove, nodeType)
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return bestScore
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proc aspirationWindow(self: var SearchManager, score: Score, depth: int): Score =
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# Aspiration windows: start subsequent searches with tighter
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# alpha-beta bounds and widen them as needed (i.e. when the score
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# goes beyond the window) to increase the number of cutoffs
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var
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delta = Score(20)
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alpha = max(lowestEval(), score - delta)
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beta = min(highestEval(), score + delta)
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searchDepth = depth
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while not self.shouldStop() or getMonoTime() >= self.softLimit:
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result = self.search(searchDepth, 0, alpha, beta, true)
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# Score is outside window bounds, widen the one that
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# we got past to get a better result
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if score <= alpha:
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beta = (alpha + beta) div 2
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alpha = max(lowestEval(), alpha - delta)
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searchDepth = depth
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elif score >= beta:
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beta = min(highestEval(), beta + delta)
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if searchDepth > 1:
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searchDepth = searchDepth - 1
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else:
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# Value was within the alpha-beta bounds, we're done
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break
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# Try again with larger window
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delta += delta div 2
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# TODO: Tune this
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if delta >= Score(500):
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# Window got too wide, give up and search with the full range
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# of alpha-beta values
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delta = highestEval()
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proc findBestMove*(self: var SearchManager, timeRemaining, increment: int64, maxDepth: int, maxNodes: uint64, searchMoves: seq[Move], timePerMove=false): Move =
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## Finds the best move in the current position
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## and returns it, limiting search time according
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@ -496,46 +519,17 @@ proc findBestMove*(self: var SearchManager, timeRemaining, increment: int64, max
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self.searchFlag[].store(true)
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# Iterative deepening loop
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var score {.used.} = Score(0)
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for i in 1..min(MAX_DEPTH, maxDepth):
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when not defined(aspirationWindows):
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self.search(i, 0, lowestEval(), highestEval(), true)
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for depth in 1..min(MAX_DEPTH, maxDepth):
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if depth < 5 or not defined(aspirationWindow):
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score = self.search(depth, 0, lowestEval(), highestEval(), true)
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else:
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if i < 5:
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score = self.search(i, 0, lowestEval(), highestEval(), true)
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else:
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# Aspiration windows: start subsequent searches with tighter
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# alpha-beta bounds and widen them as needed (i.e. when the score
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# goes beyond the window) to increase the number of cutoffs
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var
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delta = Score(15)
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alpha = max(lowestEval(), score - delta)
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beta = min(highestEval(), score + delta)
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while not self.shouldStop() or getMonoTime() >= self.softLimit:
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score = self.search(i, 0, alpha, beta, true)
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# Score is outside window bounds, widen the one that
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# we got past to get a better result
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if score <= alpha:
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beta = (alpha + beta) div 2
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alpha = max(lowestEval(), alpha - delta)
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elif score >= beta:
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beta = min(highestEval(), beta + delta)
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else:
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# Value was within the alpha-beta bounds, we're done
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break
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# Try again with larger window
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delta *= 2
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# TODO: Tune this
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if delta >= Score(500):
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# Window got too wide, give up and search with the full range
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# of alpha-beta values
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score = self.search(i, 0, lowestEval(), highestEval(), true)
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break
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score = self.aspirationWindow(score, depth)
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if self.pvMoves[0][0] != nullMove():
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result = self.pvMoves[0][0]
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if self.shouldStop():
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self.log(i - 1)
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self.log(depth - 1)
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break
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self.log(i)
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self.log(depth)
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# Soft time management: don't start a new search iteration
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# if the soft limit has expired, as it is unlikely to complete
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# anyway
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