277 lines
13 KiB
Python
277 lines
13 KiB
Python
import heapq
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import math
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import random
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import time
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from typing import Dict, List, Tuple
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from katrain.core.utils import var_to_grid
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from katrain.core.constants import (
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OUTPUT_INFO,
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OUTPUT_DEBUG,
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AI_STRATEGIES_POLICY,
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AI_POLICY,
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AI_WEIGHTED,
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AI_STRATEGIES_PICK,
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AI_JIGO,
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AI_SCORELOSS,
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AI_DEFAULT,
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AI_INFLUENCE,
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AI_LOCAL,
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AI_TENUKI,
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AI_TERRITORY,
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AI_PICK,
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AI_RANK,
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AI_HANDICAP,
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OUTPUT_ERROR,
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)
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from katrain.core.game import Game, GameNode, Move
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def weighted_selection_without_replacement(items: List[Tuple], pick_n: int) -> List[Tuple]:
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"""For a list of tuples where the second element is a weight, returns random items with those weights, without replacement."""
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elt = [(math.log(random.random()) / item[1], item) for item in items] # magic
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return [e[1] for e in heapq.nlargest(pick_n, elt)] # NB fine if too small
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def dirichlet_noise(num, dir_alpha=0.3):
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sample = [random.gammavariate(dir_alpha, 1) for _ in range(num)]
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sum_sample = sum(sample)
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return [s / sum_sample for s in sample]
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def fmt_moves(moves: List[Tuple[float, Move]]):
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return ", ".join(f"{mv.gtp()} ({p:.2%})" for p, mv in moves)
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def policy_weighted_move(policy_moves, lower_bound, weaken_fac):
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lower_bound, weaken_fac = max(0, lower_bound), max(0.01, weaken_fac)
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weighted_coords = [
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(pv, pv ** (1 / weaken_fac), move) for pv, move in policy_moves if pv > lower_bound and not move.is_pass
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]
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if weighted_coords:
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top = weighted_selection_without_replacement(weighted_coords, 1)[0]
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ai_thoughts = f"Playing policy-weighted random move {top[2].gtp()} ({top[0]:.1%}) from {len(weighted_coords)} moves above lower_bound of {lower_bound:.1%}."
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else:
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top = policy_moves[0]
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ai_thoughts = f"Playing top policy move because no non-pass move > above lower_bound of {lower_bound:.1%}."
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return top[2], ai_thoughts
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def generate_influence_territory_weights(ai_mode, ai_settings, policy_grid, size):
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thr_line = ai_settings["threshold"] - 1 # zero-based
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if ai_mode == AI_INFLUENCE:
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weight = lambda x, y: (1 / ai_settings["line_weight"]) ** (
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max(0, thr_line - min(size[0] - 1 - x, x)) + max(0, thr_line - min(size[1] - 1 - y, y))
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)
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else:
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weight = lambda x, y: (1 / ai_settings["line_weight"]) ** (
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max(0, min(size[0] - 1 - x, x, size[1] - 1 - y, y) - thr_line)
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)
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weighted_coords = [
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(policy_grid[y][x] * weight(x, y), weight(x, y), x, y)
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for x in range(size[0])
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for y in range(size[1])
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if policy_grid[y][x] > 0
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]
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ai_thoughts = f"Generated weights for {ai_mode} according to weight factor {ai_settings['line_weight']} and distance from {thr_line + 1}th line. "
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return weighted_coords, ai_thoughts
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def generate_local_tenuki_weights(ai_mode, ai_settings, policy_grid, cn, size):
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var = ai_settings["stddev"] ** 2
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mx, my = cn.move.coords
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weighted_coords = [
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(policy_grid[y][x], math.exp(-0.5 * ((x - mx) ** 2 + (y - my) ** 2) / var), x, y)
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for x in range(size[0])
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for y in range(size[1])
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if policy_grid[y][x] > 0
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]
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ai_thoughts = f"Generated weights based on one minus gaussian with variance {var} around coordinates {mx},{my}. "
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if ai_mode == AI_TENUKI:
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weighted_coords = [(p, 1 - w, x, y) for p, w, x, y in weighted_coords]
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ai_thoughts = (
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f"Generated weights based on one minus gaussian with variance {var} around coordinates {mx},{my}. "
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)
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return weighted_coords, ai_thoughts
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def request_ai_analysis(game: Game, cn: GameNode, extra_settings: Dict) -> Dict:
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error = False
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analysis = None
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def set_analysis(a):
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nonlocal analysis
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analysis = a
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def set_error(a):
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nonlocal error
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game.katrain.log("Error in PDA-based analysis", a)
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error = True
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engine = game.engines[cn.player]
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engine.request_analysis(
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cn,
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callback=set_analysis,
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error_callback=set_error,
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priority=1_000,
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ownership=False,
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extra_settings=extra_settings,
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)
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while not (error or analysis):
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time.sleep(0.01)
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engine.check_alive(exception_if_dead=True)
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return analysis
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def generate_ai_move(game: Game, ai_mode: str, ai_settings: Dict) -> Tuple[Move, GameNode]:
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cn = game.current_node
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if ai_mode == AI_HANDICAP:
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pda = ai_settings["pda"]
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if ai_settings["automatic"]:
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n_handicaps = len(game.root.get_list_property("AB", []))
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MOVE_VALUE = 14 # could be rules dependent
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b_stones_advantage = max(n_handicaps - 1, 0) - (cn.komi - MOVE_VALUE / 2) / MOVE_VALUE
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pda = min(3, max(-3, -b_stones_advantage * (3 / 8))) # max PDA at 8 stone adv, normal 9 stone game is 8.46
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handicap_analysis = request_ai_analysis(
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game, cn, {"playoutDoublingAdvantage": pda, "playoutDoublingAdvantagePla": "BLACK"}
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)
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if not handicap_analysis:
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game.katrain.log(f"Error getting handicap-based move", OUTPUT_ERROR)
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ai_mode = AI_DEFAULT
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while not cn.analysis_ready:
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time.sleep(0.01)
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game.engines[cn.next_player].check_alive(exception_if_dead=True)
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ai_thoughts = ""
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if (ai_mode in AI_STRATEGIES_POLICY) and cn.policy: # pure policy based move
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policy_moves = cn.policy_ranking
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pass_policy = cn.policy[-1]
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# dont make it jump around for the last few sensible non pass moves
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top_5_pass = any([polmove[1].is_pass for polmove in policy_moves[:5]])
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size = game.board_size
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policy_grid = var_to_grid(cn.policy, size) # type: List[List[float]]
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top_policy_move = policy_moves[0][1]
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ai_thoughts += f"Using policy based strategy, base top 5 moves are {fmt_moves(policy_moves[:5])}. "
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if (ai_mode == AI_POLICY and cn.depth <= ai_settings["opening_moves"]) or (
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ai_mode in [AI_LOCAL, AI_TENUKI] and not (cn.move and cn.move.coords)
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):
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ai_mode = AI_WEIGHTED
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ai_thoughts += f"Strategy override, using policy-weighted strategy instead. "
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ai_settings = {"pick_override": 0.9, "weaken_fac": 1, "lower_bound": 0.02}
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if top_5_pass:
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aimove = top_policy_move
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ai_thoughts += "Playing top one because one of them is pass."
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elif ai_mode == AI_POLICY:
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aimove = top_policy_move
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ai_thoughts += f"Playing top policy move {aimove.gtp()}."
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else: # weighted or pick-based
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legal_policy_moves = [(pol, mv) for pol, mv in policy_moves if not mv.is_pass and pol > 0]
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board_squares = size[0] * size[1]
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if ai_mode == AI_RANK: # calibrated, override from 0.8 at start to ~0.4 at full board
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override = 0.8 * (1 - 0.5 * (board_squares - len(legal_policy_moves)) / board_squares)
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else:
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override = ai_settings["pick_override"]
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if policy_moves[0][0] > override:
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aimove = top_policy_move
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ai_thoughts += f"Top policy move has weight > {override:.1%}, so overriding other strategies."
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elif ai_mode == AI_WEIGHTED:
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aimove, ai_thoughts = policy_weighted_move(
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policy_moves, ai_settings["lower_bound"], ai_settings["weaken_fac"]
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)
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elif ai_mode in AI_STRATEGIES_PICK:
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if ai_mode != AI_RANK:
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n_moves = int(ai_settings["pick_frac"] * len(legal_policy_moves) + ai_settings["pick_n"])
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else:
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n_moves = int(round(board_squares / 361 * len(legal_policy_moves)/(1.311648546930214*((0.31164467+0.55726218*(len(legal_policy_moves)/board_squares)*math.exp(-1*(3.0308747*(len(legal_policy_moves)/board_squares)*(len(legal_policy_moves)/board_squares)-(len(legal_policy_moves)/board_squares)-0.045792218*ai_settings["kyu_rank"]-0.31164467)**2)-0.0064860256*ai_settings["kyu_rank"])*(0.0630149+0.762399*board_squares/(10**(-0.05737*ai_settings["kyu_rank"]+1.9482))))-0.08265346672884874)))
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if ai_mode in [AI_INFLUENCE, AI_TERRITORY, AI_LOCAL, AI_TENUKI]:
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if cn.depth > ai_settings["endgame"] * board_squares:
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weighted_coords = [(pol, 1, *mv.coords) for pol, mv in legal_policy_moves]
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x_ai_thoughts = (
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f"Generated equal weights as move number >= {ai_settings['endgame'] * size[0] * size[1]}. "
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)
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n_moves = int(max(n_moves, 0.5 * len(legal_policy_moves)))
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elif ai_mode in [AI_INFLUENCE, AI_TERRITORY]:
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weighted_coords, x_ai_thoughts = generate_influence_territory_weights(
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ai_mode, ai_settings, policy_grid, size
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)
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else: # ai_mode in [AI_LOCAL, AI_TENUKI]
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weighted_coords, x_ai_thoughts = generate_local_tenuki_weights(
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ai_mode, ai_settings, policy_grid, cn, size
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)
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ai_thoughts += x_ai_thoughts
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else: # ai_mode in [AI_PICK, AI_RANK]:
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weighted_coords = [
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(policy_grid[y][x], 1, x, y)
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for x in range(size[0])
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for y in range(size[1])
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if policy_grid[y][x] > 0
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]
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pick_moves = weighted_selection_without_replacement(weighted_coords, n_moves)
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ai_thoughts += f"Picked {min(n_moves,len(weighted_coords))} random moves according to weights. "
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if pick_moves:
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new_top = [
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(p, Move((x, y), player=cn.next_player)) for p, wt, x, y in heapq.nlargest(5, pick_moves)
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]
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aimove = new_top[0][1]
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ai_thoughts += f"Top 5 among these were {fmt_moves(new_top)} and picked top {aimove.gtp()}. "
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if new_top[0][0] < pass_policy:
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ai_thoughts += f"But found pass ({pass_policy:.2%} to be higher rated than {aimove.gtp()} ({new_top[0][0]:.2%}) so will play top policy move instead."
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aimove = top_policy_move
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else:
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aimove = top_policy_move
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ai_thoughts += f"Pick policy strategy {ai_mode} failed to find legal moves, so is playing top policy move {aimove.gtp()}."
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else:
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raise ValueError(f"Unknown Policy-based AI mode {ai_mode}")
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else: # Engine based move
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candidate_ai_moves = cn.candidate_moves
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if ai_mode == AI_HANDICAP:
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candidate_ai_moves = handicap_analysis["moveInfos"]
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top_cand = Move.from_gtp(candidate_ai_moves[0]["move"], player=cn.next_player)
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if top_cand.is_pass: # don't play suicidal to balance score - pass when it's best
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aimove = top_cand
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ai_thoughts += f"Top move is pass, so passing regardless of strategy."
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else:
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if ai_mode == AI_JIGO:
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sign = cn.player_sign(cn.next_player)
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jigo_move = min(
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candidate_ai_moves, key=lambda move: abs(sign * move["scoreLead"] - ai_settings["target_score"])
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)
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aimove = Move.from_gtp(jigo_move["move"], player=cn.next_player)
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ai_thoughts += f"Jigo strategy found {len(candidate_ai_moves)} candidate moves (best {top_cand.gtp()}) and chose {aimove.gtp()} as closest to 0.5 point win"
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elif ai_mode == AI_SCORELOSS:
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c = ai_settings["strength"]
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moves = [
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(
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d["pointsLost"],
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math.exp(min(200, -c * max(0, d["pointsLost"]))),
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Move.from_gtp(d["move"], player=cn.next_player),
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)
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for d in candidate_ai_moves
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]
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topmove = weighted_selection_without_replacement(moves, 1)[0]
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aimove = topmove[2]
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ai_thoughts += f"ScoreLoss strategy found {len(candidate_ai_moves)} candidate moves (best {top_cand.gtp()}) and chose {aimove.gtp()} (weight {topmove[1]:.3f}, point loss {topmove[0]:.1f}) based on score weights."
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else:
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if ai_mode not in [AI_DEFAULT, AI_HANDICAP]:
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game.katrain.log(f"Unknown AI mode {ai_mode} or policy missing, using default.", OUTPUT_INFO)
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ai_thoughts += f"Strategy {ai_mode} not found or unexpected fallback."
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aimove = top_cand
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if ai_mode == AI_HANDICAP:
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ai_thoughts += f"Handicap strategy found {len(candidate_ai_moves)} moves returned from the engine and chose {aimove.gtp()} as top move. PDA based score {cn.format_score(handicap_analysis['rootInfo']['scoreLead'])} and win rate {cn.format_winrate(handicap_analysis['rootInfo']['winrate'])}"
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else:
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ai_thoughts += f"Default strategy found {len(candidate_ai_moves)} moves returned from the engine and chose {aimove.gtp()} as top move"
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game.katrain.log(f"AI thoughts: {ai_thoughts}", OUTPUT_DEBUG)
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played_node = game.play(aimove)
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played_node.ai_thoughts = ai_thoughts
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return aimove, played_node
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