neat implementation up to mutate
This commit is contained in:
parent
4a05baa103
commit
cf4d773c10
8 changed files with 468 additions and 144 deletions
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@ -52,6 +52,7 @@ class Client:
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self.pos = pos
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self.time = time.time()
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self.projMatrix = perspectiveMatrix(45.0, 400 / 400, 0.01, MAX_DISTANCE)
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glutReshapeFunc(self.resize)
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glutDisplayFunc(self.display)
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@ -195,7 +196,7 @@ class Client:
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glutSwapBuffers()
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print('fps', 1.0 / (time.time() - self.time))
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# print('fps', 1.0 / (time.time() - self.time))
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self.time = time.time()
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glutPostRedisplay()
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@ -1,13 +1,16 @@
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import time
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from Client.Client import Client, MAX_DISTANCE
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from Client.Client import Client, MAX_DISTANCE, glutPostRedisplay
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from MatrixStuff.Transformations import perspectiveMatrix
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from labirinth_ai.LabyrinthProvider import LabyrinthProvider
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import numpy as np
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class LabyrinthClient(Client):
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def __init__(self, test=False, pos=[0, 0, 0], world_class=LabyrinthProvider):
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self.render = True
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self.round_timer = time.time()
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super(LabyrinthClient, self).__init__(test, pos, world_class)
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def draw_world(self):
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@ -32,12 +35,25 @@ class LabyrinthClient(Client):
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self.world_provider.world.set_color(sub.x, sub.y, 0, 212 / 255.0, 150 / 255.0, 222 / 255.0)
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self.projMatrix = perspectiveMatrix(45.0, 400 / 400, 0.01, MAX_DISTANCE)
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print('redraw', time.time() - start_time)
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# print('redraw', time.time() - start_time)
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def display(self):
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if self.render:
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super(LabyrinthClient, self).display()
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self.draw_world()
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else:
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glutPostRedisplay()
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self.world_provider.world.update()
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# round_end = time.time()
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# print('round time', round_end - self.round_timer)
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# self.round_timer = round_end
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def keyboardHandler(self, key: int, x: int, y: int):
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super().keyboardHandler(key, x, y)
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if key == b' ':
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self.render = not self.render
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if __name__ == '__main__':
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client = LabyrinthClient(pos=[-50, -50, -200])
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@ -1,11 +1,11 @@
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import time
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from typing import Tuple
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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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@ -26,21 +26,37 @@ class LabyrinthWorld(World):
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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.round = 1
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self.evolve_timer = 10
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# self.evolve_timer = 1500
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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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self._hunters = None
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self._herbivores = None
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@property
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def hunters(self):
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if self._hunters is None:
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return []
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return self._hunters.subjects
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@property
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def herbivores(self):
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if self._herbivores is None:
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return []
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return self._herbivores.subjects
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@property
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def subjects(self):
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return self.hunters + self.herbivores
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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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@ -146,36 +162,40 @@ class LabyrinthWorld(World):
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# adding subjects
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from labirinth_ai.Subject import Hunter, Herbivore
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for _ in range(10):
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from labirinth_ai.Population import Population
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self._hunters = Population(Hunter, self, 10)
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self._herbivores = Population(Herbivore, self, 40)
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self.subjectDict = self.build_subject_dict()
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def generate_free_coordinates(self) -> Tuple[int, int]:
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while True:
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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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break
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for _ in range(40):
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while True:
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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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break
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return px, py
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def build_subject_dict(self):
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subject_dict = {}
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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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subject_dict[(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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subject_dict[(sub.x, sub.y)].append(sub)
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return subject_dict
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def update(self):
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if self.round % self.evolve_timer == 0:
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print('Evolve population')
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self.round = 0
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self._hunters.evolve()
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self._herbivores.evolve()
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self.subjectDict = self.build_subject_dict()
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self.round += 1
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# start = time.time()
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for sub in self.subjects:
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sub.calculateAction(self)
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@ -185,7 +205,6 @@ class LabyrinthWorld(World):
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sub.update(self)
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sub.tick += 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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@ -194,18 +213,14 @@ class LabyrinthWorld(World):
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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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if not sub.alive:
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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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@ -44,8 +44,8 @@ class BaseModel(nn.Module):
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class BaseDataSet(Dataset):
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def __init__(self, states, targets):
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assert len(states) == len(targets), "Needs to have as many states as targets!"
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self.states = torch.tensor(states, dtype=torch.float32)
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self.targets = torch.tensor(targets, dtype=torch.float32)
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self.states = torch.tensor(np.array(states), dtype=torch.float32)
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self.targets = torch.tensor(np.array(targets), dtype=torch.float32)
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def __len__(self):
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return len(self.states)
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@ -69,7 +69,7 @@ def create_loss_function(action):
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def from_numpy(x):
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return torch.tensor(x, dtype=torch.float32)
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return torch.tensor(np.array(x), dtype=torch.float32)
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def train(states, targets, model, optimizer):
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@ -3,40 +3,16 @@ from torch import nn
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import numpy as np
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import tqdm
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from torch.utils.data import Dataset, DataLoader
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from labirinth_ai.Models.BaseModel import device
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class NodeGene:
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valid_types = ['sensor', 'hidden', 'output']
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def __init__(self, node_id, node_type, bias=None):
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assert node_type in self.valid_types, 'Unknown node type!'
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self.node_id = node_id
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self.node_type = node_type
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if node_type == 'hidden':
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assert bias is not None, 'Expected a bias for hidden node types!'
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self.bias = bias
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else:
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self.bias = None
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class ConnectionGene:
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def __init__(self, start, end, enabled, innovation_num, weight=None, recurrent=False):
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self.start = start
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self.end = end
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self.enabled = enabled
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self.innvovation_num = innovation_num
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self.recurrent = recurrent
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if weight is None:
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self.weight = np.random.random(1)[0] * 2 - 1.0
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else:
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self.weight = weight
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from labirinth_ai.Models.BaseModel import device, BaseDataSet, create_loss_function, create_optimizer
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from labirinth_ai.Models.Genotype import Genotype
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class EvolutionModel(nn.Module):
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evolutionary = True
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def __init__(self, view_dimension, action_num, channels, genes=None):
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def __init__(self, view_dimension, action_num, channels, genes: Genotype = None, genotype_class=None):
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if genotype_class is None:
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genotype_class = Genotype
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super(EvolutionModel, self).__init__()
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self.flatten = nn.Flatten()
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@ -46,25 +22,29 @@ class EvolutionModel(nn.Module):
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if genes is None:
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self.num_input_nodes = channels * (2 * self.viewD + 1) * (2 * self.viewD + 1) + 2
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self.genes = genotype_class(action_num, self.num_input_nodes)
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else:
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self.num_input_nodes = len(list(filter(lambda element: element[1].node_type == 'sensor', genes.nodes.items())))
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assert self.num_input_nodes > 0, 'Network needs to have sensor nodes!'
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is_input_over = False
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is_output_over = False
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for key, node in genes.nodes.items():
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if node.node_type == 'sensor':
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if is_input_over:
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raise ValueError('Node genes need to follow the order sensor, output, hidden!')
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self.genes = {'nodes': {}, 'connections': []}
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node_id = 0
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for _ in range(self.num_input_nodes):
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self.genes['nodes'][node_id] = NodeGene(node_id, 'sensor')
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node_id += 1
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first_action = node_id
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for _ in range(action_num * 2):
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self.genes['nodes'][node_id] = NodeGene(node_id, 'output')
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node_id += 1
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if node.node_type == 'output':
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is_input_over = True
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if is_output_over:
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raise ValueError('Node genes need to follow the order sensor, output, hidden!')
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for index in range(self.num_input_nodes):
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for action in range(action_num * 2):
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self.genes['connections'].append(
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ConnectionGene(index, first_action + action, True, index*(action_num * 2) + action)
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)
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if node.node_type == 'hidden':
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is_output_over = True
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self.genes = genes
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self.incoming_connections = {}
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for connection in self.genes['connections']:
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for connection in self.genes.connections:
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if connection.end not in self.incoming_connections.keys():
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self.incoming_connections[connection.end] = []
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self.incoming_connections[connection.end].append(connection)
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@ -73,16 +53,17 @@ class EvolutionModel(nn.Module):
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self.indices = {}
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self.has_recurrent = False
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non_recurrent_indices = {}
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self.non_recurrent_indices = {}
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self.recurrent_indices = {}
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with torch.no_grad():
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for key, value in self.incoming_connections.items():
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value.sort(key=lambda element: element.start)
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lin = nn.Linear(len(value), 1, bias=self.genes['nodes'][key].bias is not None)
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lin = nn.Linear(len(value), 1, bias=self.genes.nodes[key].bias is not None)
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for index, connection in enumerate(value):
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lin.weight[0, index] = value[index].weight
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if self.genes['nodes'][key].bias is not None:
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lin.bias[0] = self.genes['nodes'][key].bias
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if self.genes.nodes[key].bias is not None:
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lin.bias[0] = self.genes.nodes[key].bias
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non_lin = nn.ELU()
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sequence = nn.Sequential(
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@ -93,15 +74,17 @@ class EvolutionModel(nn.Module):
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self.layers[key] = sequence
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self.indices[key] = list(map(lambda element: element.start, value))
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non_recurrent_indices[key] = list(filter(lambda element: not element.recurrent, value))
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if not self.has_recurrent and len(non_recurrent_indices[key]) != len(self.indices[key]):
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self.non_recurrent_indices[key] = list(filter(lambda element: not element.recurrent, value))
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self.recurrent_indices[key] = list(filter(lambda element: element.recurrent, value))
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if not self.has_recurrent and len(self.non_recurrent_indices[key]) != len(self.indices[key]):
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self.has_recurrent = True
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non_recurrent_indices[key] = list(map(lambda element: element.start, non_recurrent_indices[key]))
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self.non_recurrent_indices[key] = list(map(lambda element: element.start, self.non_recurrent_indices[key]))
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self.recurrent_indices[key] = list(map(lambda element: element.start, self.recurrent_indices[key]))
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rank_of_node = {}
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for i in range(self.num_input_nodes):
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rank_of_node[i] = 0
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layers_to_add = list(non_recurrent_indices.items())
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layers_to_add = list(self.non_recurrent_indices.items())
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while len(layers_to_add) > 0:
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for index, (key, incoming_nodes) in enumerate(list(layers_to_add)):
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max_rank = -1
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ranked_layers = list(rank_of_node.items())
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ranked_layers.sort(key=lambda element: element[1])
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ranked_layers = list(filter(lambda element: element[1] > 0, ranked_layers))
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self.layer_order = list(map(lambda element: element[0], ranked_layers))
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self.memory = torch.Tensor((max(map(lambda element: element[1].node_id, self.genes['nodes'].items())) + 1))
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def forward(self, x, memory=None):
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ranked_layers = list(map(lambda element: (element, 0),
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filter(lambda recurrent_element:
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recurrent_element not in list(
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map(lambda ranked_layer: ranked_layer[0], ranked_layers)
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),
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list(filter(lambda recurrent_keys:
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len(self.recurrent_indices[recurrent_keys]) > 0,
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self.recurrent_indices.keys()))))) + ranked_layers
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self.layer_order = list(map(lambda element: element[0], ranked_layers))
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self.memory_size = (max(map(lambda element: element[1].node_id, self.genes.nodes.items())) + 1)
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self.memory = torch.Tensor(self.memory_size)
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self.output_range = range(self.num_input_nodes, self.num_input_nodes + self.action_num * 2)
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def forward(self, x, last_memory=None):
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x_flat = self.flatten(x)
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if memory is None:
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memory = torch.Tensor(self.memory)
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if last_memory is not None:
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last_memory_flat = self.flatten(last_memory)
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elif self.has_recurrent:
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raise ValueError('Recurrent networks need to be passed their previous memory!')
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memory = torch.Tensor(self.memory_size)
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outs = []
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for batch_element in x_flat:
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for batch_index, batch_element in enumerate(x_flat):
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memory[0:self.num_input_nodes] = batch_element
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for layer_index in self.layer_order:
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memory[layer_index] = self.layers[layer_index](memory[self.indices[layer_index]])
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non_recurrent_in = memory[self.non_recurrent_indices[layer_index]]
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non_recurrent_in = torch.stack([non_recurrent_in])
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if self.has_recurrent and len(self.recurrent_indices[layer_index]) > 0:
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recurrent_in = last_memory_flat[batch_index, self.recurrent_indices[layer_index]]
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recurrent_in = torch.stack([recurrent_in])
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combined_in = torch.concat([non_recurrent_in, recurrent_in], dim=1)
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else:
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combined_in = non_recurrent_in
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memory[layer_index] = self.layers[layer_index](combined_in)
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outs.append(memory[self.num_input_nodes: self.num_input_nodes + self.action_num * 2])
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outs = torch.stack(outs)
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self.memory = torch.Tensor(memory)
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return torch.reshape(outs, (x.shape[0], 4, 2))
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else:
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memory[:, 0:self.num_input_nodes] = x
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for layer_index in self.layer_order:
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memory[:, layer_index] = self.layers[layer_index](memory[:, self.indices[layer_index]])
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return torch.reshape(
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memory[:, self.num_input_nodes: self.num_input_nodes + self.action_num * 2],
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(x.shape[0], 4, 2))
|
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return torch.reshape(outs, (x.shape[0], outs.shape[1]//2, 2))
|
||||
|
||||
def update_genes_with_weights(self):
|
||||
for key, value in self.incoming_connections.items():
|
||||
value.sort(key=lambda element: element.start)
|
||||
|
||||
sequence = self.layers[key]
|
||||
lin = sequence[0]
|
||||
for index, connection in enumerate(value):
|
||||
value[index].weight = float(lin.weight[0, index])
|
||||
if self.genes.nodes[key].bias is not None:
|
||||
self.genes.nodes[key].bias = float(lin.bias[0])
|
||||
|
||||
|
||||
|
||||
class RecurrentDataSet(BaseDataSet):
|
||||
def __init__(self, states, targets, memory):
|
||||
super().__init__(states, targets)
|
||||
assert len(states) == len(memory), "Needs to have as many states as memories!"
|
||||
self.memory = torch.tensor(np.array(memory), dtype=torch.float32)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
return self.states[idx], self.memory[idx], self.targets[idx]
|
||||
|
||||
|
||||
def train_recurrent(states, memory, targets, model, optimizer):
|
||||
for action in range(model.action_num):
|
||||
data_set = RecurrentDataSet(states[action], targets[action], memory[action])
|
||||
dataloader = DataLoader(data_set, batch_size=64, shuffle=True)
|
||||
loss_fn = create_loss_function(action)
|
||||
|
||||
size = len(dataloader)
|
||||
model.train()
|
||||
for batch, (X, M, y) in enumerate(dataloader):
|
||||
X, y, M = X.to(device), y.to(device), M.to(device)
|
||||
|
||||
# Compute prediction error
|
||||
pred = model(X, M)
|
||||
loss = loss_fn(pred, y)
|
||||
|
||||
# Backpropagation
|
||||
optimizer.zero_grad()
|
||||
loss.backward(retain_graph=True)
|
||||
optimizer.step()
|
||||
|
||||
if batch % 100 == 0:
|
||||
loss, current = loss.item(), batch * len(X)
|
||||
print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
|
||||
model.eval()
|
||||
|
||||
del data_set
|
||||
del dataloader
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = np.random.random((1, 486))
|
||||
sample = np.random.random((1, 1))
|
||||
last_memory = np.zeros((1, 3))
|
||||
|
||||
model = EvolutionModel(5, 4, 4).to(device)
|
||||
print(model)
|
||||
from labirinth_ai.Models.Genotype import NodeGene, ConnectionGene, Genotype
|
||||
genes = Genotype(nodes={0: NodeGene(0, 'sensor'), 1: NodeGene(1, 'output'), 2: NodeGene(2, 'hidden', 1)},
|
||||
connections=[ConnectionGene(0, 2, True, 0, recurrent=True), ConnectionGene(2, 1, True, 1, 1)])
|
||||
|
||||
model = EvolutionModel(1, 1, 1, genes)
|
||||
|
||||
model = model.to(device)
|
||||
# print(model)
|
||||
print(model.has_recurrent)
|
||||
|
||||
test = model(torch.tensor(sample, dtype=torch.float32))
|
||||
test = model(torch.tensor(sample, dtype=torch.float32), torch.tensor(last_memory, dtype=torch.float32))
|
||||
# test = test.cpu().detach().numpy()
|
||||
print(test)
|
||||
# print(test)
|
||||
|
||||
state = np.random.random((1, 486))
|
||||
target = np.random.random((4, 2))
|
||||
state = np.random.random((1, 1))
|
||||
memory = np.random.random((1, 1))
|
||||
|
||||
target = np.random.random((2, 1))
|
||||
states = [
|
||||
[state],
|
||||
[state],
|
||||
|
@ -170,7 +232,12 @@ if __name__ == '__main__':
|
|||
[target],
|
||||
[target],
|
||||
]
|
||||
memories = [
|
||||
[memory],
|
||||
[memory],
|
||||
[memory],
|
||||
[memory],
|
||||
]
|
||||
|
||||
optimizer = torch.optim.RMSprop(model.parameters(), lr=1e-3)
|
||||
from labirinth_ai.Models.BaseModel import train
|
||||
train(states, targets, model, optimizer)
|
||||
train_recurrent(states, memories, targets, model, optimizer)
|
||||
|
|
139
labirinth_ai/Models/Genotype.py
Normal file
139
labirinth_ai/Models/Genotype.py
Normal file
|
@ -0,0 +1,139 @@
|
|||
from abc import abstractmethod
|
||||
from typing import List, Dict
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
class NodeGene:
|
||||
valid_types = ['sensor', 'hidden', 'output']
|
||||
|
||||
def __init__(self, node_id, node_type, bias=None):
|
||||
assert node_type in self.valid_types, 'Unknown node type!'
|
||||
self.node_id = node_id
|
||||
self.node_type = node_type
|
||||
if node_type == 'hidden':
|
||||
assert bias is not None, 'Expected a bias for hidden node types!'
|
||||
self.bias = bias
|
||||
else:
|
||||
self.bias = None
|
||||
|
||||
|
||||
class ConnectionGene:
|
||||
def __init__(self, start, end, enabled, innovation_num, weight=None, recurrent=False):
|
||||
self.start = start
|
||||
self.end = end
|
||||
self.enabled = enabled
|
||||
self.innvovation_num = innovation_num
|
||||
self.recurrent = recurrent
|
||||
if weight is None:
|
||||
self.weight = np.random.random(1)[0] * 2 - 1.0
|
||||
else:
|
||||
self.weight = weight
|
||||
|
||||
|
||||
class Genotype:
|
||||
def __init__(self, action_num: int = None, num_input_nodes: int = None,
|
||||
nodes: Dict[int, NodeGene] = None, connections: List[ConnectionGene] = None):
|
||||
self.nodes = {}
|
||||
self.connections = []
|
||||
if action_num is not None and num_input_nodes is not None:
|
||||
node_id = 0
|
||||
for _ in range(num_input_nodes):
|
||||
self.nodes[node_id] = NodeGene(node_id, 'sensor')
|
||||
node_id += 1
|
||||
first_action = node_id
|
||||
for _ in range(action_num * 2):
|
||||
self.nodes[node_id] = NodeGene(node_id, 'output')
|
||||
node_id += 1
|
||||
|
||||
for index in range(num_input_nodes):
|
||||
for action in range(action_num * 2):
|
||||
self.connections.append(
|
||||
ConnectionGene(index, first_action + action, True, index * (action_num * 2) + action)
|
||||
)
|
||||
if nodes is not None and connections is not None:
|
||||
self.nodes = nodes
|
||||
self.connections = connections
|
||||
|
||||
def calculate_rank_of_nodes(self):
|
||||
rank_of_node = {}
|
||||
nodes_to_rank = list(self.nodes.items())
|
||||
while len(nodes_to_rank) > 0:
|
||||
for list_index, (id, node) in enumerate(nodes_to_rank):
|
||||
incoming_connections = list(filter(lambda connection: connection.end == id and
|
||||
not connection.recurrent, self.connections))
|
||||
if len(incoming_connections) == 0:
|
||||
rank_of_node[id] = 0
|
||||
nodes_to_rank.pop(list_index)
|
||||
break
|
||||
|
||||
incoming_connections_starts = list(map(lambda connection: connection.start, incoming_connections))
|
||||
start_ranks = list(map(lambda element: rank_of_node[element[0]],
|
||||
filter(lambda start_node: start_node[0] in incoming_connections_starts and
|
||||
start_node[0] in rank_of_node.keys(),
|
||||
self.nodes.items())))
|
||||
if len(start_ranks) == len(incoming_connections):
|
||||
rank_of_node[id] = max(start_ranks) + 1
|
||||
nodes_to_rank.pop(list_index)
|
||||
break
|
||||
return rank_of_node
|
||||
|
||||
@abstractmethod
|
||||
def mutate(self, innovation_num) -> int:
|
||||
"""
|
||||
Decides whether or not to mutate this network. Then returns the new innovation number.
|
||||
:param innovation_num: Current innovation number
|
||||
:return: Updated innovation number
|
||||
"""
|
||||
|
||||
# return innovation_num
|
||||
raise NotImplementedError()
|
||||
|
||||
@abstractmethod
|
||||
def cross(self, other):
|
||||
raise NotImplementedError()
|
||||
# return self
|
||||
|
||||
|
||||
class NeatLike(Genotype):
|
||||
connection_add_thr = 0.3
|
||||
node_add_thr = 0.3
|
||||
|
||||
def mutate(self, innovation_num, allow_recurrent=False) -> int:
|
||||
"""
|
||||
Decides whether or not to mutate this network. Then returns the new innovation number.
|
||||
:param allow_recurrent: Optional parameter allowing or disallowing recurrent connections to form
|
||||
:param innovation_num: Current innovation number
|
||||
:return: Updated innovation number
|
||||
"""
|
||||
# add connection
|
||||
if np.random.random(1)[0] < self.connection_add_thr or True:
|
||||
nodes = list(self.nodes.keys())
|
||||
rank_of_node = self.calculate_rank_of_nodes()
|
||||
end_nodes = list(filter(lambda node: rank_of_node[node] > 0, nodes))
|
||||
|
||||
connection_tuple = list(map(lambda connection: (connection.start, connection.end), self.connections))
|
||||
|
||||
start = np.random.randint(0, len(nodes))
|
||||
end = np.random.randint(0, len(end_nodes))
|
||||
|
||||
tries = 50
|
||||
while (rank_of_node[end_nodes[end]] == 0 or
|
||||
((not allow_recurrent) and rank_of_node[nodes[start]] > rank_of_node[end_nodes[end]])
|
||||
or nodes[start] == end_nodes[end] or (nodes[start], end_nodes[end]) in connection_tuple) and\
|
||||
tries > 0:
|
||||
end = np.random.randint(0, len(end_nodes))
|
||||
if (not allow_recurrent) and rank_of_node[nodes[start]] > rank_of_node[end_nodes[end]]:
|
||||
start = np.random.randint(0, len(nodes))
|
||||
tries -= 1
|
||||
if tries > 0:
|
||||
innovation_num += 1
|
||||
self.connections.append(
|
||||
ConnectionGene(nodes[start], end_nodes[end], True, innovation_num,
|
||||
recurrent=rank_of_node[nodes[start]] > rank_of_node[end_nodes[end]]))
|
||||
#todo add node
|
||||
|
||||
return innovation_num
|
||||
|
||||
def cross(self, other):
|
||||
return self
|
97
labirinth_ai/Population.py
Normal file
97
labirinth_ai/Population.py
Normal file
|
@ -0,0 +1,97 @@
|
|||
import random
|
||||
import numpy as np
|
||||
|
||||
from labirinth_ai.Models.Genotype import NeatLike
|
||||
|
||||
|
||||
def fib(n):
|
||||
if n == 0:
|
||||
return [1]
|
||||
elif n < 0:
|
||||
return [0]
|
||||
else:
|
||||
return [fib(n - 1)[0] + fib(n - 2)[0]] + fib(n - 1)
|
||||
|
||||
|
||||
class Population:
|
||||
def __init__(self, subject_class, world, subject_number):
|
||||
self.subjects = []
|
||||
self.world = world
|
||||
for _ in range(subject_number):
|
||||
px, py = self.world.generate_free_coordinates()
|
||||
self.subjects.append(subject_class(px, py, genotype_class=NeatLike))
|
||||
self.subject_number = subject_number
|
||||
self.subject_class = subject_class
|
||||
|
||||
def select(self):
|
||||
ranked = list(self.subjects)
|
||||
ranked.sort(key=lambda subject: subject.accumulated_rewards, reverse=True)
|
||||
|
||||
return ranked[:int(self.subject_number / 2)]
|
||||
|
||||
@classmethod
|
||||
def scatter(cls, n, buckets):
|
||||
out = np.zeros(buckets)
|
||||
if n == 0:
|
||||
return out
|
||||
|
||||
fib_number = 0
|
||||
fibs = fib(fib_number)
|
||||
while np.sum(fibs) <= n and len(fibs) <= buckets:
|
||||
fib_number += 1
|
||||
fibs = fib(fib_number)
|
||||
fib_number -= 1
|
||||
fibs = fib(fib_number)
|
||||
|
||||
for bucket in range(buckets):
|
||||
if bucket < len(fibs):
|
||||
out[bucket] += fibs[bucket]
|
||||
else:
|
||||
break
|
||||
|
||||
return out + cls.scatter(n - np.sum(fibs), buckets)
|
||||
|
||||
def evolve(self):
|
||||
# get updated weights from the models
|
||||
for subject in self.subjects:
|
||||
subject.model.update_genes_with_weights()
|
||||
|
||||
# crossbreed the current pop
|
||||
best_subjects = self.select()
|
||||
distribution = list(self.scatter(self.subject_number - int(self.subject_number / 2), int(self.subject_number / 2)))
|
||||
|
||||
new_subjects = list(best_subjects)
|
||||
for index, offspring_num in enumerate(distribution):
|
||||
for _ in range(int(offspring_num)):
|
||||
parent_1 = best_subjects[index]
|
||||
parent_2 = best_subjects[random.randint(index + 1, len(best_subjects) - 1)]
|
||||
|
||||
new_genes = parent_1.model.genes.cross(parent_2.model.genes)
|
||||
|
||||
# position doesn't matter, since mutation will set it
|
||||
new_subject = self.subject_class(0, 0, new_genes)
|
||||
new_subject.history = parent_1.history
|
||||
new_subject.samples = parent_1.samples + parent_2.samples
|
||||
new_subjects.append(new_subject)
|
||||
|
||||
assert len(new_subjects) == self.subject_number, 'All generations should have constant size!'
|
||||
|
||||
# mutate the pop
|
||||
mutated_subjects = []
|
||||
innovation_num = max(map(lambda subject: max(map(lambda connection: connection.innvovation_num,
|
||||
subject.model.genes.connections
|
||||
)
|
||||
)
|
||||
, new_subjects))
|
||||
for subject in new_subjects:
|
||||
subject.accumulated_rewards = 0
|
||||
|
||||
innovation_num = subject.model.genes.mutate(innovation_num)
|
||||
|
||||
px, py = self.world.generate_free_coordinates()
|
||||
new_subject = self.subject_class(px, py, subject.model.genes)
|
||||
new_subject.history = subject.history
|
||||
new_subject.samples = subject.samples
|
||||
mutated_subjects.append(new_subject)
|
||||
|
||||
self.subjects = mutated_subjects
|
|
@ -4,6 +4,7 @@ import tensorflow as tf
|
|||
from tensorflow import keras
|
||||
|
||||
from labirinth_ai.LabyrinthWorld import LabyrinthWorld
|
||||
from labirinth_ai.Models.EvolutionModel import EvolutionModel
|
||||
from labirinth_ai.loss import loss2, loss3
|
||||
from labirinth_ai.Models.BaseModel import BaseModel, train, create_optimizer, device, from_numpy
|
||||
|
||||
|
@ -350,7 +351,7 @@ class NetLearner(Subject):
|
|||
|
||||
self.strikes = 0
|
||||
|
||||
def __init__(self, x, y):
|
||||
def __init__(self, x, y, genes=None, genotype_class=None):
|
||||
super(NetLearner, self).__init__(x, y)
|
||||
|
||||
self.action = None
|
||||
|
@ -370,7 +371,10 @@ class NetLearner(Subject):
|
|||
self.x_in = []
|
||||
self.actions = []
|
||||
self.target = []
|
||||
self.model = BaseModel(self.viewD, 4, 4).to(device)
|
||||
|
||||
# self.model = BaseModel(self.viewD, 4, 4).to(device)
|
||||
self.model = EvolutionModel(self.viewD, 4, 4, genes=genes, genotype_class=genotype_class).to(device)
|
||||
|
||||
self.optimizer = create_optimizer(self.model)
|
||||
|
||||
if len(self.samples) < self.randomBuffer:
|
||||
|
@ -540,9 +544,11 @@ class NetLearner(Subject):
|
|||
|
||||
# if len(self.samples) % self.batchsize == 0 and len(self.samples) >= self.randomBuffer:
|
||||
if len(self.samples) > self.nextTrain and doTrain:
|
||||
print('train')
|
||||
print('train', len(self.samples))
|
||||
self.train()
|
||||
self.nextTrain = len(self.samples)
|
||||
self.nextTrain = min(self.batchsize + self.nextTrain, (self.historySizeMul + 1) * self.batchsize)
|
||||
print(len(self.samples), self.nextTrain)
|
||||
|
||||
self.accumulated_rewards += self.lastReward
|
||||
|
||||
|
@ -657,23 +663,6 @@ class Herbivore(NetLearner):
|
|||
|
||||
samples = []
|
||||
|
||||
# x_in = keras.Input(shape=(4 * (2 * viewD + 1) * (2 * viewD + 1) + 2))
|
||||
# target = keras.Input(shape=(10, 1))
|
||||
# inVec = keras.layers.Flatten()(x_in)
|
||||
# # kernel_regularizer=keras.regularizers.l2(0.01)
|
||||
# actions = keras.layers.Dense((4 * (2 * viewD + 1) * (2 * viewD + 1)), activation='elu')(inVec)
|
||||
# actions = keras.layers.Dense(((2 * viewD + 1) * (2 * viewD + 1)), activation='elu')(actions)
|
||||
# actions = keras.layers.Dense(8, activation='linear', use_bias=False)(actions)
|
||||
# # actions = keras.layers.Dense(4, activation='linear', use_bias=False)(inVec)
|
||||
#
|
||||
# model = keras.Model(inputs=x_in, outputs=actions)
|
||||
#
|
||||
# # model.compile(optimizer='adam', loss=loss2, target_tensors=[target])
|
||||
# model.compile(optimizer=tf.keras.optimizers.RMSprop(learningRate), loss=loss2, target_tensors=[target])
|
||||
|
||||
# def __init__(self, x, y):
|
||||
# super(Herbivore, self).__init__(x, y)
|
||||
|
||||
def createState(self, world: LabyrinthWorld):
|
||||
state = np.zeros((2 * self.viewD + 1, 2 * self.viewD + 1), np.float) # - 1
|
||||
state2 = np.zeros((2 * self.viewD + 1, 2 * self.viewD + 1), np.float) # - 1
|
||||
|
|
Loading…
Reference in a new issue