233 lines
9.5 KiB
Python
233 lines
9.5 KiB
Python
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import time
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from Objects.Cube.Cube import Cube
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from Objects.World import World
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import numpy as np
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import random
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class LabyrinthWorld(World):
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randomBuffer = 0
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batchsize = 1000
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randomBuffer = max(4 * batchsize, randomBuffer)
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def __init__(self, chunk_size_x: int, chunk_size_y: int, chunk_size_z: int,
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chunk_n_x: int, chunk_n_y: int, chunk_n_z: int, programs: dict):
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self.board_shape = (chunk_size_x * chunk_n_x, chunk_size_y * chunk_n_y)
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self.board = np.zeros(self.board_shape)
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super(LabyrinthWorld, self).__init__(chunk_size_x, chunk_size_y, chunk_size_z,
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chunk_n_x, chunk_n_y, chunk_n_z, programs)
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self.max_room_dim = 20
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self.min_room_dim = 6
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self.max_room_num = 32
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self.max_corridors = 4 * self.max_room_num
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self.max_crates = self.max_room_num
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self.subjects = []
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self.ins = []
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self.actions = []
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self.targets = []
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self.model = None
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self.lastUpdate = time.time()
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self.nextTrain = self.randomBuffer
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self.round = 0
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self.trailMix = np.zeros(self.board_shape)
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self.grass = np.zeros(self.board_shape)
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self.hunter_grass = np.zeros(self.board_shape)
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self.subjectDict = {}
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def generate(self, seed: int = None, sea_plate_height: int = 50, continental_plate_height: int = 200):
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board = np.zeros(self.board_shape)
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random.seed(seed)
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np.random.seed(seed)
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# find random starting point
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px = random.randint(self.max_room_dim, (self.board_shape[0] - 1) - self.max_room_dim)
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py = random.randint(self.max_room_dim, (self.board_shape[1] - 1) - self.max_room_dim)
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# 0, 0 is top left
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right = (1, 0)
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left = (-1, 0)
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up = (0, -1)
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down = (0, 1)
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# place rooms
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room_num = 0
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corridor_num = 0
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while room_num < self.max_room_num and corridor_num < self.max_corridors:
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# try to place Room
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w = random.randint(self.min_room_dim, self.max_room_dim)
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h = random.randint(self.min_room_dim, self.max_room_dim)
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can_place_room = np.sum(
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board[px - int(w / 2.0):px + int(w / 2.0), py - int(h / 2.0):py + int(h / 2.0)] == 1) == 0 and px - int(
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w / 2.0) >= 0 and px + int(w / 2.0) < self.board_shape[0] and \
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py - int(h / 2.0) >= 0 and py + int(h / 2.0) < self.board_shape[1]
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if can_place_room:
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# place Room
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board[px - int(w / 2.0):px + int(w / 2.0), py - int(h / 2.0):py + int(h / 2.0)] = 1
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room_num += 1
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else:
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# move && place Corridor
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directions = []
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while len(directions) == 0:
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movable = []
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corridor_length = random.randint(self.min_room_dim, self.max_room_dim)
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if px - corridor_length >= 0:
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movable.append(left)
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if board[px - 1, py] != 2:
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directions.append(left)
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if px + corridor_length < self.board_shape[0]:
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movable.append(right)
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if board[px + 1, py] != 2:
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directions.append(right)
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if py - corridor_length >= 0:
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movable.append(up)
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if board[px, py - 1] != 2:
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directions.append(up)
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if py + corridor_length < self.board_shape[1]:
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movable.append(down)
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if board[px, py + 1] != 2:
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directions.append(down)
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if len(directions) != 0:
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if len(directions) > 1:
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d = directions[random.randint(0, len(directions) - 1)]
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else:
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d = directions[0]
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changed = False
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for _ in range(corridor_length):
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if board[px, py] != 1 and board[px, py] != 2:
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board[px, py] = 2
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if (-d[0], -d[1]) not in movable or board[px - d[0], py - d[1]] != 2:
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changed = True
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px += d[0]
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py += d[1]
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if changed:
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corridor_num += 1
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else:
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if len(movable) != 0:
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if len(movable) > 1:
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d = movable[random.randint(0, len(movable) - 1)]
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else:
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d = movable[0]
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for _ in range(corridor_length):
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px += d[0]
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py += d[1]
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crates = 0
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while crates < self.max_crates:
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px = random.randint(0, (self.board_shape[0] - 1))
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py = random.randint(0, (self.board_shape[1] - 1))
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if board[px, py] == 1:
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board[px, py] = 3
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crates += 1
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board[board == 2] = 1
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print((room_num, self.max_room_num))
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print((corridor_num, self.max_corridors))
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self.board = board
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# setting up the board
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for x_pos in range(0, self.board_shape[0]):
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for y_pos in range(0, self.board_shape[1]):
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for z_pos in range(0, 1):
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self.put_object(x_pos, y_pos, z_pos, Cube().setColor(1, 1, 1))
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# adding subjects
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from labirinth_ai.Subject import Hunter, Herbivore
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while len(self.subjects) < 2:
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px = random.randint(self.max_room_dim, self.board_shape[0] - self.max_room_dim)
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py = random.randint(self.max_room_dim, self.board_shape[1] - self.max_room_dim)
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if self.board[px, py] == 1:
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self.subjects.append(Hunter(px, py))
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self.ins += self.subjects[-1].x_in
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self.actions += self.subjects[-1].actions
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self.targets += self.subjects[-1].target
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while len(self.subjects) < 10:
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px = random.randint(self.max_room_dim, self.board_shape[0] - self.max_room_dim)
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py = random.randint(self.max_room_dim, self.board_shape[1] - self.max_room_dim)
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if self.board[px, py] == 1:
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self.subjects.append(Herbivore(px, py))
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self.ins += self.subjects[-1].x_in
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self.actions += self.subjects[-1].actions
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self.targets += self.subjects[-1].target
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for x in range(self.board_shape[0]):
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for y in range(self.board_shape[1]):
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self.subjectDict[(x, y)] = []
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for sub in self.subjects:
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self.subjectDict[(sub.x, sub.y)].append(sub)
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def update(self):
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# start = time.time()
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if self.model is None:
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for sub in self.subjects:
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sub.calculateAction(self)
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else:
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states = list(map(lambda e: e.createState(self), self.subjects))
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states = sum(list(map(lambda e: [e, e, e, e], states)), [])
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vals = self.model.predict(states)
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vals = np.reshape(np.transpose(np.reshape(vals, (len(self.subjects), 4, 2)), (0, 2, 1)),
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(len(self.subjects), 1, 8))
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list(map(lambda e: e[1].calculateAction(self, vals[e[0]], states[e[0]]), enumerate(self.subjects)))
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for sub in self.subjects:
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if sub.alive:
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sub.update(self, doTrain=self.model is None)
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sub.tick += 1
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if self.model is not None:
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if self.round >= self.nextTrain:
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samples = list(map(lambda e: e.generateSamples(), self.subjects))
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states = sum(list(map(lambda e: e[0], samples)), [])
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targets = sum(list(map(lambda e: e[1], samples)), [])
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self.model.fit(states, targets)
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self.nextTrain = self.batchsize / 5
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self.round = 0
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for sub in self.subjects:
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if len(sub.samples) > 20*self.batchsize:
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sub.samples = sub.samples[:-20*self.batchsize]
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else:
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self.round += 1
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new_subjects = []
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kill_table = {}
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live_table = {}
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for sub in self.subjects:
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if sub.name not in kill_table.keys():
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kill_table[sub.name] = 0
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live_table[sub.name] = 0
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kill_table[sub.name] += sub.kills
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live_table[sub.name] += sub.lives
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if sub.alive:
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new_subjects.append(sub)
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else:
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px = random.randint(self.max_room_dim, (self.board_shape[0] - 1) - self.max_room_dim)
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py = random.randint(self.max_room_dim, (self.board_shape[1] - 1) - self.max_room_dim)
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while self.board[px, py] == 0:
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px = random.randint(self.max_room_dim, (self.board_shape[0] - 1) - self.max_room_dim)
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py = random.randint(self.max_room_dim, (self.board_shape[1] - 1) - self.max_room_dim)
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sub.respawnUpdate(px, py, self)
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new_subjects.append(sub)
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self.subjects = new_subjects
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self.trailMix *= 0.99
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self.grass = np.minimum(self.grass + 0.01 * (self.board != 0), 3)
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self.hunter_grass = np.minimum(self.hunter_grass + 0.01 * (self.board != 0), 3)
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self.trailMix *= (self.trailMix > 0.01)
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