Merge pull request 'development' (#12) from development into master
Reviewed-on: #12
This commit is contained in:
commit
3dd86ea96b
@ -1,14 +1,15 @@
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import pygame
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import pygame
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from settings import SCREEN_WIDTH, SCREEN_HEIGHT
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from survival.ai.genetic_algorithm import GeneticAlgorithm
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from survival.camera import Camera
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from survival.components.inventory_component import InventoryComponent
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from survival.components.inventory_component import InventoryComponent
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from survival.game_map import GameMap
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from survival.game.game_map import GameMap
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from survival.generators.building_generator import BuildingGenerator
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from survival.generators.building_generator import BuildingGenerator
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from survival.generators.player_generator import PlayerGenerator
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from survival.generators.player_generator import PlayerGenerator
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from survival.generators.resource_generator import ResourceGenerator
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from survival.generators.resource_generator import ResourceGenerator
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from survival.generators.world_generator import WorldGenerator
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from survival.generators.world_generator import WorldGenerator
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from survival.settings import SCREEN_WIDTH, SCREEN_HEIGHT, MUTATE_NETWORKS, LEARN
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from survival.systems.draw_system import DrawSystem
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from survival.systems.draw_system import DrawSystem
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from survival.systems.neural_system import NeuralSystem
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class Game:
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class Game:
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@ -16,10 +17,17 @@ class Game:
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self.world_generator = WorldGenerator(win, self.reset)
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self.world_generator = WorldGenerator(win, self.reset)
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self.game_map, self.world, self.camera = self.world_generator.create_world()
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self.game_map, self.world, self.camera = self.world_generator.create_world()
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self.run = True
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self.run = True
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if LEARN and MUTATE_NETWORKS:
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self.genetic_algorithm = GeneticAlgorithm(self.world.get_processor(NeuralSystem), self.finish_training)
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def reset(self):
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def reset(self):
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if LEARN and MUTATE_NETWORKS:
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self.genetic_algorithm.train()
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self.world_generator.reset_world()
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self.world_generator.reset_world()
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def finish_training(self):
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self.run = False
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def update(self, ms):
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def update(self, ms):
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events = pygame.event.get()
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events = pygame.event.get()
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@ -47,4 +55,4 @@ if __name__ == '__main__':
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game = Game()
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game = Game()
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while game.run:
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while game.run:
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game.update(clock.tick(60))
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game.update(clock.tick(500))
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BIN
survival/ai/decision_tree/classifier.joblib
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BIN
survival/ai/decision_tree/classifier.joblib
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60
survival/ai/decision_tree/decision_tree.py
Normal file
60
survival/ai/decision_tree/decision_tree.py
Normal file
@ -0,0 +1,60 @@
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import json
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from joblib import dump, load
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from matplotlib import pyplot as plt
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from sklearn import tree
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from sklearn.feature_extraction import DictVectorizer
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class DecisionTree:
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def __init__(self):
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self.clf = None
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self.vec = None
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def build(self, depth: int):
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path = "tree_data.json"
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samples = []
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results = []
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with open(path, "r") as training_file:
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for sample in training_file:
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sample, result = self.process_input(sample)
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samples.append(sample)
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results.append(result)
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self.vec = DictVectorizer()
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self.clf = tree.DecisionTreeClassifier(max_depth=depth)
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self.clf = self.clf.fit(self.vec.fit_transform(samples).toarray(), results)
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def save_model(self, clf_file, vec_file):
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dump(self.clf, clf_file)
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dump(self.vec, vec_file)
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def load_model(self, clf_file, vec_file):
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self.clf = load(clf_file)
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self.vec = load(vec_file)
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def predict_answer(self, params):
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return self.clf.predict(self.vec.transform(params).toarray())
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def plot_tree(self):
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print('Plotting tree...')
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fig = plt.figure(figsize=(36, 27))
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_ = tree.plot_tree(self.clf,
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feature_names=self.vec.get_feature_names(),
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filled=True)
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fig.savefig("decistion_tree.png")
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print('Success!')
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@staticmethod
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def process_input(line):
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data = json.loads(line.strip())
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result = data['result']
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del data['result']
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del data['food_result']
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del data['water_result']
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del data['wood_result']
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sample = data
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return sample, result
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124
survival/ai/decision_tree/decision_tree_data.py
Normal file
124
survival/ai/decision_tree/decision_tree_data.py
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@ -0,0 +1,124 @@
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import random
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from typing import Dict
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from survival.ai.decision_tree.decision_tree import DecisionTree
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from survival.generators.resource_type import ResourceType
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class TreeDataGenerator:
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INV_RANGE = (1, 100)
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VISIBLE = (True, False)
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DISTANCE_RANGE = (3, 7)
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DISTANCE_FACTOR = 0.2
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COUNT = (1, 2, 3)
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def generate(self, count=1000):
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full_data = []
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self.process(count, full_data)
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self.write_data_to_file(full_data)
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return full_data
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def process(self, count, full_data):
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for i in range(count):
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# if i % 10000 == 0:
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# print(i)
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package = {}
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# Create resource data for each resource type.
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for resource in ResourceType:
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package[resource] = self.create_resource_data()
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# Get the resource with highest result among all generated resource types.
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best_resource = self.get_best_resource(package)
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# Unpack packaged resources.
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(food, water, wood) = (
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package[ResourceType.FOOD], package[ResourceType.WATER], package[ResourceType.WOOD])
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# Create dictionary filled with data.
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data = {"food_inv": food[0], 'food_visible': str(food[1]), 'food_distance': food[2],
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'food_count': food[3], 'food_result': food[4],
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'water_inv': water[0], 'water_visible': str(water[1]), 'water_distance': water[2],
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'water_count': water[3], 'water_result': water[4],
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'wood_inv': wood[0], 'wood_visible': str(wood[1]), 'wood_distance': wood[2],
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'wood_count': wood[3], 'wood_result': wood[4],
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'result': best_resource.name.lower()}
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full_data.append(data)
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@staticmethod
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def write_data_to_file(full_data):
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print("Writing to file...")
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# Open the target file to which the data will be saved and write all the data to it.
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with open('tree_data.json', 'w') as f:
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for data in full_data:
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data_str = str(data).replace("'", '"').replace('"False"', 'false').replace('"True"', 'true')
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f.write(data_str)
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f.write('\n')
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print("Success!")
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def create_resource_data(self):
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is_visible = random.choice(self.VISIBLE)
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inventory = random.randint(min(self.INV_RANGE), max(self.INV_RANGE))
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if is_visible:
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cnt = random.choice(self.COUNT)
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distance = random.randint(min(self.DISTANCE_RANGE), max(self.DISTANCE_RANGE))
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else:
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cnt = 0
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distance = 0
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# Equation determining the results processed by decision tree.
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result = (self.INV_RANGE[1] / inventory) * (1 * cnt if is_visible else 0.9) + (
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max(self.DISTANCE_RANGE) / distance if is_visible else 0.5) * self.DISTANCE_FACTOR
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return [inventory, is_visible, distance, cnt, result]
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@staticmethod
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def get_best_resource(package: Dict) -> ResourceType:
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best_resource = None
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for resource, data in package.items():
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if best_resource is None or data[:-1] < package[best_resource][:-1]:
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best_resource = resource
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return best_resource
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@staticmethod
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def print_data(full_data):
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for data in full_data:
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print(TreeDataGenerator.format_words(["Data", "Apple", "Water", "Wood"]))
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print(TreeDataGenerator.format_words(["Inventory", data["food_inv"], data["water_inv"], data["wood_inv"]]))
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print(TreeDataGenerator.format_words(
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["Visible", data["food_visible"], data["water_visible"], data["wood_visible"]]))
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print(TreeDataGenerator.format_words(
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["Distance", data["food_distance"], data["water_distance"], data["wood_distance"]]))
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print(
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TreeDataGenerator.format_words(["Count", data["food_count"], data["water_count"], data["wood_count"]]))
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print(TreeDataGenerator.format_words(
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["Result", round(data["food_result"], 3), round(data["water_result"], 3),
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round(data["wood_result"], 3)]))
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print(f'Best resource: {data["result"]}')
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print('--------------------------------------------------------------')
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@staticmethod
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def format_words(words):
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return '{:>12} {:>12} {:>12} {:>12}'.format(words[0], words[1], words[2], words[3])
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# Train tree
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generator = TreeDataGenerator()
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data = generator.generate(50000)
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generator.print_data(data)
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tree = DecisionTree()
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tree.build(1000)
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tree.plot_tree()
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tree.save_model('classifier.joblib', 'vectorizer.joblib')
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# ----------------------------------------------------------- #
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# Use trained tree
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# tree = DecisionTree()
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# tree.load_model('classifier.joblib', 'vectorizer.joblib')
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#
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# answ = tree.predict_answer({'food_inv': 40, 'water_inv': 10, 'wood_inv': 20,
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# 'food_distance': 2, 'water_distance': -1, 'wood_distance': 4,
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# 'food_visible': True, 'water_visible': False, 'wood_visible': True,
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# 'food_count': 1, 'water_count': 1, 'wood_count': 1})
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# print(answ)
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BIN
survival/ai/decision_tree/decistion_tree.png
Normal file
BIN
survival/ai/decision_tree/decistion_tree.png
Normal file
Binary file not shown.
After Width: | Height: | Size: 722 KiB |
50000
survival/ai/decision_tree/tree_data.json
Normal file
50000
survival/ai/decision_tree/tree_data.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
survival/ai/decision_tree/vectorizer.joblib
Normal file
BIN
survival/ai/decision_tree/vectorizer.joblib
Normal file
Binary file not shown.
84
survival/ai/genetic_algorithm.py
Normal file
84
survival/ai/genetic_algorithm.py
Normal file
@ -0,0 +1,84 @@
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import sys
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from survival.ai.model import LinearQNetwork
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from survival.ai.optimizer import Optimizer
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class GeneticAlgorithm:
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GAMES_PER_NETWORK = 40
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PLOTS_COUNTER = 0
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CURRENT_GENERATION = 1
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def __init__(self, neural_system, callback):
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self.callback = callback
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self.logs_file = open('genetic_logs.txt', 'w')
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self.original_stdout = sys.stdout
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sys.stdout = self.logs_file
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self.neural_system = neural_system
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self.generations = 20
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self.population = 10 # Minimum 5 needed to allow breeding
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self.nn_params = {
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'neurons': [128, 192, 256, 384, 512],
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'layers': [0, 1, 2, 3],
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'activation': ['relu', 'elu', 'tanh'],
|
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|
'ratio': [0.0007, 0.0009, 0.0011, 0.0013, 0.0015],
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'optimizer': ['RMSprop', 'Adam', 'SGD', 'Adagrad', 'Adadelta'],
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|
}
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self.optimizer = Optimizer(self.nn_params)
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self.networks: list[LinearQNetwork] = self.optimizer.create_population(self.population)
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self.finished = False
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self.trained_counter = 0
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self.iterations = 0
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self.trained_generations = 0
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print('Started generation 1...')
|
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self.change_network(self.networks[0])
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|
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def train(self):
|
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|
if self.iterations < GeneticAlgorithm.GAMES_PER_NETWORK - 1:
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|
self.iterations += 1
|
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|
return
|
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|
self.iterations = 0
|
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|
print(f'Network score: {self.optimizer.fitness(self.networks[self.trained_counter])}')
|
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|
self.trained_counter += 1
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|
# If all networks in current population were trained
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|
if self.trained_counter >= self.population:
|
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|
# Get average score in current population
|
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|
avg_score = self.calculate_average_score(self.networks)
|
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|
print(f'Average population score: {avg_score}.')
|
||||||
|
|
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|
results_file = open('genetic_results.txt', 'w')
|
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|
for network in self.networks:
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|
results_file.write(
|
||||||
|
f'Network {network.id} params {network.network_params}. Avg score = {sum(network.scores) / len(network.scores)}\n')
|
||||||
|
results_file.close()
|
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|
if self.trained_generations >= self.generations - 1:
|
||||||
|
# Sort the final population
|
||||||
|
self.networks = sorted(self.networks, key=lambda x: sum(x.scores) / len(x.scores), reverse=True)
|
||||||
|
self.finished = True
|
||||||
|
self.logs_file.close()
|
||||||
|
sys.stdout = self.original_stdout
|
||||||
|
self.callback()
|
||||||
|
return
|
||||||
|
|
||||||
|
self.trained_generations += 1
|
||||||
|
GeneticAlgorithm.CURRENT_GENERATION = self.trained_generations + 1
|
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|
print(f'Started generation {GeneticAlgorithm.CURRENT_GENERATION}...')
|
||||||
|
self.networks = self.optimizer.evolve(self.networks)
|
||||||
|
self.trained_counter = 0
|
||||||
|
self.change_network(self.networks[self.trained_counter])
|
||||||
|
|
||||||
|
def calculate_average_score(self, networks):
|
||||||
|
sums = 0
|
||||||
|
lengths = 0
|
||||||
|
for network in networks:
|
||||||
|
sums += self.optimizer.fitness(network)
|
||||||
|
lengths += len(network.scores)
|
||||||
|
return sums / lengths
|
||||||
|
|
||||||
|
def change_network(self, net):
|
||||||
|
GeneticAlgorithm.PLOTS_COUNTER += 1
|
||||||
|
print(f"Changed network to {GeneticAlgorithm.PLOTS_COUNTER} {net.network_params}")
|
||||||
|
self.logs_file.flush()
|
||||||
|
net.id = GeneticAlgorithm.PLOTS_COUNTER
|
||||||
|
self.neural_system.load_model(net)
|
@ -6,8 +6,9 @@ from survival.components.direction_component import DirectionChangeComponent
|
|||||||
from survival.components.moving_component import MovingComponent
|
from survival.components.moving_component import MovingComponent
|
||||||
from survival.components.position_component import PositionComponent
|
from survival.components.position_component import PositionComponent
|
||||||
from survival.components.resource_component import ResourceComponent
|
from survival.components.resource_component import ResourceComponent
|
||||||
from survival.enums import Direction
|
from survival.game.enums import Direction
|
||||||
from survival.esper import World
|
from survival.esper import World
|
||||||
|
from survival.systems.consumption_system import ConsumeComponent
|
||||||
|
|
||||||
|
|
||||||
class Action(Enum):
|
class Action(Enum):
|
||||||
@ -35,9 +36,11 @@ class Action(Enum):
|
|||||||
if action == Action.ROTATE_LEFT:
|
if action == Action.ROTATE_LEFT:
|
||||||
world.add_component(entity, DirectionChangeComponent(
|
world.add_component(entity, DirectionChangeComponent(
|
||||||
Direction.rotate_left(world.component_for_entity(entity, PositionComponent).direction)))
|
Direction.rotate_left(world.component_for_entity(entity, PositionComponent).direction)))
|
||||||
|
world.add_component(entity, ConsumeComponent(0.2))
|
||||||
elif action == Action.ROTATE_RIGHT:
|
elif action == Action.ROTATE_RIGHT:
|
||||||
world.add_component(entity, DirectionChangeComponent(
|
world.add_component(entity, DirectionChangeComponent(
|
||||||
Direction.rotate_right(world.component_for_entity(entity, PositionComponent).direction)))
|
Direction.rotate_right(world.component_for_entity(entity, PositionComponent).direction)))
|
||||||
|
world.add_component(entity, ConsumeComponent(0.2))
|
||||||
else:
|
else:
|
||||||
world.add_component(entity, MovingComponent())
|
world.add_component(entity, MovingComponent())
|
||||||
return action
|
return action
|
@ -2,10 +2,12 @@ import numpy as np
|
|||||||
from IPython import display
|
from IPython import display
|
||||||
from matplotlib import pyplot as plt
|
from matplotlib import pyplot as plt
|
||||||
|
|
||||||
|
from survival.settings import MUTATE_NETWORKS
|
||||||
|
from survival.ai.genetic_algorithm import GeneticAlgorithm
|
||||||
from survival.components.learning_component import LearningComponent
|
from survival.components.learning_component import LearningComponent
|
||||||
from survival.components.position_component import PositionComponent
|
from survival.components.position_component import PositionComponent
|
||||||
from survival.enums import Direction
|
from survival.game.enums import Direction
|
||||||
from survival.graph_search import Action
|
from survival.ai.graph_search import Action
|
||||||
|
|
||||||
|
|
||||||
class LearningUtils:
|
class LearningUtils:
|
||||||
@ -23,8 +25,8 @@ class LearningUtils:
|
|||||||
self.plot_mean_scores.append(mean_score)
|
self.plot_mean_scores.append(mean_score)
|
||||||
|
|
||||||
def plot(self):
|
def plot(self):
|
||||||
display.clear_output(wait=True)
|
# display.clear_output(wait=True)
|
||||||
display.display(plt.gcf())
|
# display.display(plt.gcf())
|
||||||
plt.clf()
|
plt.clf()
|
||||||
plt.title('Results')
|
plt.title('Results')
|
||||||
plt.xlabel('Number of Games')
|
plt.xlabel('Number of Games')
|
||||||
@ -35,9 +37,12 @@ class LearningUtils:
|
|||||||
plt.text(len(self.plot_scores) - 1, self.plot_scores[-1], str(self.plot_scores[-1]))
|
plt.text(len(self.plot_scores) - 1, self.plot_scores[-1], str(self.plot_scores[-1]))
|
||||||
plt.text(len(self.plot_mean_scores) - 1, self.plot_mean_scores[-1], str(self.plot_mean_scores[-1]))
|
plt.text(len(self.plot_mean_scores) - 1, self.plot_mean_scores[-1], str(self.plot_mean_scores[-1]))
|
||||||
self.plots += 1
|
self.plots += 1
|
||||||
|
if MUTATE_NETWORKS:
|
||||||
|
plt.savefig(f'model/plots/{GeneticAlgorithm.PLOTS_COUNTER}_{self.plots}.png')
|
||||||
|
else:
|
||||||
plt.savefig(f'model/plots/{self.plots}.png')
|
plt.savefig(f'model/plots/{self.plots}.png')
|
||||||
plt.show(block=False)
|
plt.show(block=False)
|
||||||
plt.pause(.1)
|
# plt.pause(.1)
|
||||||
|
|
||||||
def append_action(self, action: Action, pos: PositionComponent):
|
def append_action(self, action: Action, pos: PositionComponent):
|
||||||
self.last_actions.append([action, pos.grid_position])
|
self.last_actions.append([action, pos.grid_position])
|
@ -1,4 +1,5 @@
|
|||||||
import os
|
import os
|
||||||
|
import random
|
||||||
|
|
||||||
import torch
|
import torch
|
||||||
from torch import nn, optim
|
from torch import nn, optim
|
||||||
@ -6,16 +7,46 @@ import torch.nn.functional as functional
|
|||||||
|
|
||||||
|
|
||||||
class LinearQNetwork(nn.Module):
|
class LinearQNetwork(nn.Module):
|
||||||
def __init__(self, input_size, hidden_size, output_size, pretrained=False):
|
TORCH_ACTiVATIONS = 'tanh'
|
||||||
|
|
||||||
|
def __init__(self, nn_params, input_size, output_size, randomize=True, params=None):
|
||||||
super().__init__()
|
super().__init__()
|
||||||
self.linear_one = nn.Linear(input_size, hidden_size)
|
self.id = 0
|
||||||
self.linear_two = nn.Linear(hidden_size, output_size)
|
if params is None:
|
||||||
self.pretrained = pretrained
|
params = {}
|
||||||
|
self.params_choice = nn_params
|
||||||
|
self.scores = []
|
||||||
|
self.network_params = params
|
||||||
|
if randomize:
|
||||||
|
self.randomize()
|
||||||
|
self.layers = nn.ModuleList()
|
||||||
|
if self.network_params['layers'] == 0:
|
||||||
|
self.layers.append(nn.Linear(input_size, output_size))
|
||||||
|
else:
|
||||||
|
self.layers.append(nn.Linear(input_size, self.network_params['neurons']))
|
||||||
|
|
||||||
|
for i in range(self.network_params['layers'] - 1):
|
||||||
|
self.layers.append(nn.Linear(self.network_params['neurons'], self.network_params['neurons']))
|
||||||
|
if self.network_params['layers'] > 0:
|
||||||
|
self.ending_linear = nn.Linear(self.network_params['neurons'], output_size)
|
||||||
|
self.layers.append(self.ending_linear)
|
||||||
|
|
||||||
|
if self.network_params['activation'] in self.TORCH_ACTiVATIONS:
|
||||||
|
self.forward_func = getattr(torch, self.network_params['activation'])
|
||||||
|
else:
|
||||||
|
self.forward_func = getattr(functional, self.network_params['activation'])
|
||||||
|
|
||||||
|
def randomize(self):
|
||||||
|
"""
|
||||||
|
Sets random parameters for network.
|
||||||
|
"""
|
||||||
|
for key in self.params_choice:
|
||||||
|
self.network_params[key] = random.choice(self.params_choice[key])
|
||||||
|
|
||||||
def forward(self, x):
|
def forward(self, x):
|
||||||
x = functional.relu(self.linear_one(x))
|
for i in range(len(self.layers) - 1):
|
||||||
x = self.linear_two(x)
|
x = self.forward_func(self.layers[i](x))
|
||||||
|
x = self.layers[-1](x)
|
||||||
return x
|
return x
|
||||||
|
|
||||||
def save(self, file_name='model.pth'):
|
def save(self, file_name='model.pth'):
|
||||||
@ -27,23 +58,24 @@ class LinearQNetwork(nn.Module):
|
|||||||
torch.save(self.state_dict(), file_path)
|
torch.save(self.state_dict(), file_path)
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def load(input_size, hidden_size, output_size, file_name='model.pth'):
|
def load(params, input_size, output_size, file_name='model.pth'):
|
||||||
model_directory = 'model'
|
model_directory = 'model'
|
||||||
file_path = os.path.join(model_directory, file_name)
|
file_path = os.path.join(model_directory, file_name)
|
||||||
if os.path.isfile(file_path):
|
if os.path.isfile(file_path):
|
||||||
model = LinearQNetwork(input_size, hidden_size, output_size, True)
|
model = LinearQNetwork(params, input_size, output_size, True)
|
||||||
model.load_state_dict(torch.load(file_path))
|
model.load_state_dict(torch.load(file_path))
|
||||||
model.eval()
|
model.eval()
|
||||||
return model
|
return model
|
||||||
return LinearQNetwork(11, 256, 3)
|
raise Exception(f'Could not find file {file_path}.')
|
||||||
|
|
||||||
|
|
||||||
class QTrainer:
|
class QTrainer:
|
||||||
def __init__(self, model, lr, gamma):
|
def __init__(self, model, lr, gamma, optimizer):
|
||||||
self.model = model
|
self.model = model
|
||||||
self.lr = lr
|
self.lr = lr
|
||||||
self.gamma = gamma
|
self.gamma = gamma
|
||||||
self.optimizer = optim.Adam(model.parameters(), lr=self.lr)
|
self.optimizer = getattr(optim, optimizer)(model.parameters(), lr=self.lr)
|
||||||
|
# self.optimizer = optim.Adam(model.parameters(), lr=self.lr)
|
||||||
self.criterion = nn.MSELoss() # Mean squared error
|
self.criterion = nn.MSELoss() # Mean squared error
|
||||||
|
|
||||||
def train_step(self, state, action, reward, next_state, done):
|
def train_step(self, state, action, reward, next_state, done):
|
144
survival/ai/optimizer.py
Normal file
144
survival/ai/optimizer.py
Normal file
@ -0,0 +1,144 @@
|
|||||||
|
from functools import reduce
|
||||||
|
from operator import add
|
||||||
|
import random
|
||||||
|
from typing import List
|
||||||
|
|
||||||
|
from survival.ai.model import LinearQNetwork
|
||||||
|
from survival.settings import NEURAL_INPUT_SIZE, NEURAL_OUTPUT_SIZE
|
||||||
|
|
||||||
|
|
||||||
|
class Optimizer:
|
||||||
|
def __init__(self, params, retain=0.4,
|
||||||
|
random_select=0.1, mutation_chance=0.2):
|
||||||
|
self.mutation_chance = mutation_chance
|
||||||
|
self.random_select = random_select
|
||||||
|
self.retain = retain
|
||||||
|
self.nn_params = params
|
||||||
|
|
||||||
|
def create_population(self, count: int):
|
||||||
|
"""
|
||||||
|
Creates 'count' networks from random parameters.
|
||||||
|
:param count:
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
pop = []
|
||||||
|
for _ in range(0, count):
|
||||||
|
# Create a random network.
|
||||||
|
network = LinearQNetwork(self.nn_params, NEURAL_INPUT_SIZE, NEURAL_OUTPUT_SIZE)
|
||||||
|
# Add network to the population.
|
||||||
|
pop.append(network)
|
||||||
|
|
||||||
|
return pop
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def fitness(network: LinearQNetwork):
|
||||||
|
return sum(network.scores) / len(network.scores)
|
||||||
|
|
||||||
|
def grade(self, pop: List[LinearQNetwork]) -> float:
|
||||||
|
"""
|
||||||
|
Finds average fitness for given population.
|
||||||
|
"""
|
||||||
|
summed = reduce(add, (self.fitness(network) for network in pop))
|
||||||
|
return summed / float((len(pop)))
|
||||||
|
|
||||||
|
def breed(self, parent_one, parent_two):
|
||||||
|
"""
|
||||||
|
Creates a new network from given parents.
|
||||||
|
:param parent_one:
|
||||||
|
:param parent_two:
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
children = []
|
||||||
|
for _ in range(2):
|
||||||
|
|
||||||
|
child = {}
|
||||||
|
|
||||||
|
# Loop through the parameters and pick params for the kid.
|
||||||
|
for param in self.nn_params:
|
||||||
|
child[param] = random.choice(
|
||||||
|
[parent_one.network_params[param], parent_two.network_params[param]]
|
||||||
|
)
|
||||||
|
|
||||||
|
# Create new network object.
|
||||||
|
network = LinearQNetwork(self.nn_params, NEURAL_INPUT_SIZE, NEURAL_OUTPUT_SIZE)
|
||||||
|
network.network_params = child
|
||||||
|
|
||||||
|
children.append(network)
|
||||||
|
|
||||||
|
return children
|
||||||
|
|
||||||
|
def mutate(self, network: LinearQNetwork):
|
||||||
|
"""
|
||||||
|
Randomly mutates one parameter of the given network.
|
||||||
|
:param network:
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
mutation = random.choice(list(self.nn_params.keys()))
|
||||||
|
|
||||||
|
# Mutate one of the params.
|
||||||
|
network.network_params[mutation] = random.choice(self.nn_params[mutation])
|
||||||
|
|
||||||
|
return network
|
||||||
|
|
||||||
|
def evolve(self, pop):
|
||||||
|
"""
|
||||||
|
Evolves a population of networks.
|
||||||
|
"""
|
||||||
|
# Get scores for each network.
|
||||||
|
scores = [(self.fitness(network), network) for network in pop]
|
||||||
|
|
||||||
|
# Sort the scores.
|
||||||
|
scores = [x[1] for x in sorted(scores, key=lambda x: x[0], reverse=True)]
|
||||||
|
|
||||||
|
# Get the number we want to keep for the next gen.
|
||||||
|
retain_length = int(len(scores) * self.retain)
|
||||||
|
|
||||||
|
# Keep the best networks as parents for next generation.
|
||||||
|
parents = scores[:retain_length]
|
||||||
|
|
||||||
|
# Keep some other networks
|
||||||
|
for network in scores[retain_length:]:
|
||||||
|
if self.random_select > random.random():
|
||||||
|
parents.append(network)
|
||||||
|
|
||||||
|
# Reset kept networks
|
||||||
|
reseted_networks = []
|
||||||
|
for network in parents:
|
||||||
|
net = LinearQNetwork(self.nn_params, NEURAL_INPUT_SIZE, NEURAL_OUTPUT_SIZE)
|
||||||
|
net.network_params = network.network_params
|
||||||
|
reseted_networks.append(net)
|
||||||
|
|
||||||
|
parents = reseted_networks
|
||||||
|
|
||||||
|
# Randomly mutate some of the networks.
|
||||||
|
for parent in parents:
|
||||||
|
if self.mutation_chance > random.random():
|
||||||
|
parent = self.mutate(parent)
|
||||||
|
|
||||||
|
# Determine the number of freed spots for the next generation.
|
||||||
|
parents_length = len(parents)
|
||||||
|
desired_length = len(pop) - parents_length
|
||||||
|
children = []
|
||||||
|
|
||||||
|
# Fill missing spots with new children.
|
||||||
|
while len(children) < desired_length:
|
||||||
|
# Get random parents.
|
||||||
|
p1 = random.randint(0, parents_length - 1)
|
||||||
|
p2 = random.randint(0, parents_length - 1)
|
||||||
|
|
||||||
|
# Ensure they are not the same network.
|
||||||
|
if p1 != p2:
|
||||||
|
p1 = parents[p1]
|
||||||
|
p2 = parents[p2]
|
||||||
|
|
||||||
|
# Breed networks.
|
||||||
|
babies = self.breed(p1, p2)
|
||||||
|
|
||||||
|
# Add children one at a time.
|
||||||
|
for baby in babies:
|
||||||
|
# Don't grow larger than the desired length.
|
||||||
|
if len(children) < desired_length:
|
||||||
|
children.append(baby)
|
||||||
|
|
||||||
|
parents.extend(children)
|
||||||
|
return parents
|
93
survival/ai/test.py
Normal file
93
survival/ai/test.py
Normal file
@ -0,0 +1,93 @@
|
|||||||
|
import torch
|
||||||
|
import pygad
|
||||||
|
from pygad.torchga import torchga
|
||||||
|
|
||||||
|
|
||||||
|
def fitness_func(solution, sol_idx):
|
||||||
|
global data_inputs, data_outputs, torch_ga, model, loss_function
|
||||||
|
|
||||||
|
model_weights_dict = torchga.model_weights_as_dict(model=model,
|
||||||
|
weights_vector=solution)
|
||||||
|
|
||||||
|
# Use the current solution as the model parameters.
|
||||||
|
model.load_state_dict(model_weights_dict)
|
||||||
|
|
||||||
|
predictions = model(data_inputs)
|
||||||
|
abs_error = loss_function(predictions, data_outputs).detach().numpy() + 0.00000001
|
||||||
|
|
||||||
|
solution_fitness = 1.0 / abs_error
|
||||||
|
|
||||||
|
return solution_fitness
|
||||||
|
|
||||||
|
def callback_generation(ga_instance):
|
||||||
|
print("Generation = {generation}".format(generation=ga_instance.generations_completed))
|
||||||
|
print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1]))
|
||||||
|
|
||||||
|
# Create the PyTorch model.
|
||||||
|
input_layer = torch.nn.Linear(3, 2)
|
||||||
|
relu_layer = torch.nn.ReLU()
|
||||||
|
output_layer = torch.nn.Linear(2, 1)
|
||||||
|
|
||||||
|
model = torch.nn.Sequential(input_layer,
|
||||||
|
relu_layer,
|
||||||
|
output_layer)
|
||||||
|
# print(model)
|
||||||
|
|
||||||
|
# Create an instance of the pygad.torchga.TorchGA class to build the initial population.
|
||||||
|
torch_ga = torchga.TorchGA(model=model,
|
||||||
|
num_solutions=10)
|
||||||
|
|
||||||
|
loss_function = torch.nn.L1Loss()
|
||||||
|
|
||||||
|
# Data inputs
|
||||||
|
data_inputs = torch.tensor([[0.02, 0.1, 0.15],
|
||||||
|
[0.7, 0.6, 0.8],
|
||||||
|
[1.5, 1.2, 1.7],
|
||||||
|
[3.2, 2.9, 3.1]])
|
||||||
|
|
||||||
|
# Data outputs
|
||||||
|
data_outputs = torch.tensor([[0.1],
|
||||||
|
[0.6],
|
||||||
|
[1.3],
|
||||||
|
[2.5]])
|
||||||
|
|
||||||
|
# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class
|
||||||
|
num_generations = 250 # Number of generations.
|
||||||
|
num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
|
||||||
|
initial_population = torch_ga.population_weights # Initial population of network weights
|
||||||
|
parent_selection_type = "sss" # Type of parent selection.
|
||||||
|
crossover_type = "single_point" # Type of the crossover operator.
|
||||||
|
mutation_type = "random" # Type of the mutation operator.
|
||||||
|
mutation_percent_genes = 10 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists.
|
||||||
|
keep_parents = -1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.
|
||||||
|
|
||||||
|
ga_instance = pygad.GA(num_generations=num_generations,
|
||||||
|
num_parents_mating=num_parents_mating,
|
||||||
|
initial_population=initial_population,
|
||||||
|
fitness_func=fitness_func,
|
||||||
|
parent_selection_type=parent_selection_type,
|
||||||
|
crossover_type=crossover_type,
|
||||||
|
mutation_type=mutation_type,
|
||||||
|
mutation_percent_genes=mutation_percent_genes,
|
||||||
|
keep_parents=keep_parents,
|
||||||
|
on_generation=callback_generation)
|
||||||
|
|
||||||
|
ga_instance.run()
|
||||||
|
|
||||||
|
# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations.
|
||||||
|
ga_instance.plot_result(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4)
|
||||||
|
|
||||||
|
# Returning the details of the best solution.
|
||||||
|
solution, solution_fitness, solution_idx = ga_instance.best_solution()
|
||||||
|
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
|
||||||
|
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
|
||||||
|
|
||||||
|
# Fetch the parameters of the best solution.
|
||||||
|
best_solution_weights = torchga.model_weights_as_dict(model=model,
|
||||||
|
weights_vector=solution)
|
||||||
|
model.load_state_dict(best_solution_weights)
|
||||||
|
predictions = model(data_inputs)
|
||||||
|
print("Predictions : \n", predictions.detach().numpy())
|
||||||
|
|
||||||
|
abs_error = loss_function(predictions, data_outputs)
|
||||||
|
print("Absolute Error : ", abs_error.detach().numpy())
|
@ -1,5 +1,6 @@
|
|||||||
|
from survival.generators.resource_type import ResourceType
|
||||||
|
|
||||||
|
|
||||||
class ConsumptionComponent:
|
class ConsumptionComponent:
|
||||||
def __init__(self, inventory_state=0):
|
def __init__(self):
|
||||||
self.timer_value: float = 2000
|
self.status = {ResourceType.FOOD: 1, ResourceType.WOOD: 1, ResourceType.WATER: 1}
|
||||||
self.timer: float = self.timer_value
|
|
||||||
self.last_inventory_state = inventory_state
|
|
||||||
|
@ -1,4 +1,4 @@
|
|||||||
from survival.enums import Direction
|
from survival.game.enums import Direction
|
||||||
|
|
||||||
|
|
||||||
class DirectionChangeComponent:
|
class DirectionChangeComponent:
|
||||||
|
@ -17,6 +17,9 @@ class InventoryComponent:
|
|||||||
if self.items[item] < 0:
|
if self.items[item] < 0:
|
||||||
self.items[item] = 0
|
self.items[item] = 0
|
||||||
|
|
||||||
|
def count(self, item):
|
||||||
|
return self.items[item]
|
||||||
|
|
||||||
def has_item(self, item):
|
def has_item(self, item):
|
||||||
return item in self.items and self.items[item] != 0
|
return item in self.items and self.items[item] != 0
|
||||||
|
|
||||||
|
@ -7,9 +7,9 @@ class OnCollisionComponent:
|
|||||||
callbacks = []
|
callbacks = []
|
||||||
self.callbacks = callbacks
|
self.callbacks = callbacks
|
||||||
|
|
||||||
def callAll(self):
|
def call_all(self):
|
||||||
for func in self.callbacks:
|
for func in self.callbacks:
|
||||||
func()
|
func()
|
||||||
|
|
||||||
def addCallback(self, fn, **kwargs):
|
def add_callback(self, fn, **kwargs):
|
||||||
self.callbacks.append(partial(fn, **kwargs))
|
self.callbacks.append(partial(fn, **kwargs))
|
@ -1,4 +1,4 @@
|
|||||||
from survival.enums import Direction
|
from survival.game.enums import Direction
|
||||||
|
|
||||||
|
|
||||||
class PositionComponent:
|
class PositionComponent:
|
||||||
|
@ -1,4 +1,4 @@
|
|||||||
from survival.image import Image
|
from survival.game.image import Image
|
||||||
|
|
||||||
|
|
||||||
class SpriteComponent:
|
class SpriteComponent:
|
||||||
|
@ -1,48 +0,0 @@
|
|||||||
import json
|
|
||||||
import os
|
|
||||||
|
|
||||||
from sklearn import tree
|
|
||||||
from sklearn.feature_extraction import DictVectorizer
|
|
||||||
|
|
||||||
from survival.components.resource_component import ResourceComponent
|
|
||||||
|
|
||||||
|
|
||||||
class DecisionTree:
|
|
||||||
def __init__(self):
|
|
||||||
self.clf = None
|
|
||||||
self.vec = None
|
|
||||||
|
|
||||||
def build(self, depth: int):
|
|
||||||
path = os.path.join("..", "data.txt")
|
|
||||||
|
|
||||||
samples = list()
|
|
||||||
results = list()
|
|
||||||
|
|
||||||
with open(path, "r") as training_file:
|
|
||||||
for sample in training_file:
|
|
||||||
sample, result = self.process_input(sample)
|
|
||||||
samples.append(sample)
|
|
||||||
results.append(result)
|
|
||||||
|
|
||||||
self.vec = DictVectorizer()
|
|
||||||
self.clf = tree.DecisionTreeClassifier(max_depth=depth)
|
|
||||||
self.clf = self.clf.fit(self.vec.fit_transform(samples).toarray(), results)
|
|
||||||
# print(tree.export_text(self.clf, feature_names=self.vec.get_feature_names()))
|
|
||||||
|
|
||||||
def predict_answer(self, resource: ResourceComponent):
|
|
||||||
params = {
|
|
||||||
"weight": resource.weight,
|
|
||||||
"eatable": resource.eatable,
|
|
||||||
"toughness": resource.toughness
|
|
||||||
}
|
|
||||||
return self.clf.predict(self.vec.transform(params).toarray())
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def process_input(line):
|
|
||||||
data = json.loads(line.strip())
|
|
||||||
result = data['resource']
|
|
||||||
del data['resource']
|
|
||||||
sample = data
|
|
||||||
|
|
||||||
return sample, result
|
|
||||||
|
|
@ -1,4 +1,4 @@
|
|||||||
from survival.biomes.biome_preset import BiomePreset
|
from survival.game.biomes.biome_preset import BiomePreset
|
||||||
|
|
||||||
|
|
||||||
class BiomeData:
|
class BiomeData:
|
@ -1,7 +1,7 @@
|
|||||||
import random
|
import random
|
||||||
from typing import List
|
from typing import List
|
||||||
|
|
||||||
from survival.tile import Tile
|
from survival.game.tile import Tile
|
||||||
|
|
||||||
|
|
||||||
class BiomePreset:
|
class BiomePreset:
|
@ -1,6 +1,6 @@
|
|||||||
from pygame.rect import Rect
|
from pygame.rect import Rect
|
||||||
|
|
||||||
from survival import SCREEN_WIDTH, SCREEN_HEIGHT
|
from survival.settings import SCREEN_WIDTH, SCREEN_HEIGHT
|
||||||
|
|
||||||
|
|
||||||
class Camera:
|
class Camera:
|
@ -1,10 +1,10 @@
|
|||||||
from survival.components.position_component import PositionComponent
|
from survival.components.position_component import PositionComponent
|
||||||
from survival.components.resource_component import ResourceComponent
|
from survival.components.resource_component import ResourceComponent
|
||||||
from survival.entity_layer import EntityLayer
|
from survival.game.entity_layer import EntityLayer
|
||||||
from survival.esper import World
|
from survival.esper import World
|
||||||
from survival.graph_search import graph_search
|
from survival.ai.graph_search import graph_search
|
||||||
from survival.settings import AGENT_VISION_RANGE
|
from survival.settings import AGENT_VISION_RANGE
|
||||||
from survival.tile_layer import TileLayer
|
from survival.game.tile_layer import TileLayer
|
||||||
|
|
||||||
|
|
||||||
class GameMap:
|
class GameMap:
|
@ -6,7 +6,7 @@ import pygame
|
|||||||
class Image:
|
class Image:
|
||||||
def __init__(self, filename='', pos=(0, 0), scale=1, surface=None):
|
def __init__(self, filename='', pos=(0, 0), scale=1, surface=None):
|
||||||
if surface is None:
|
if surface is None:
|
||||||
self.texture = pygame.image.load(os.path.join('..', 'assets', filename)).convert_alpha()
|
self.texture = pygame.image.load(os.path.join('../', 'assets', filename)).convert_alpha()
|
||||||
else:
|
else:
|
||||||
self.texture = surface
|
self.texture = surface
|
||||||
self.image = self.texture
|
self.image = self.texture
|
@ -1,6 +1,6 @@
|
|||||||
from survival.generators.tile_generator import TileGenerator
|
from survival.generators.tile_generator import TileGenerator
|
||||||
from survival.image import Image
|
from survival.game.image import Image
|
||||||
from survival.tile import Tile
|
from survival.game.tile import Tile
|
||||||
|
|
||||||
|
|
||||||
class TileLayer:
|
class TileLayer:
|
||||||
@ -8,7 +8,6 @@ class TileLayer:
|
|||||||
self.width = width
|
self.width = width
|
||||||
self.height = height
|
self.height = height
|
||||||
self.tiles: list[list[Tile]] = TileGenerator.generate_biome_tiles(width, height)
|
self.tiles: list[list[Tile]] = TileGenerator.generate_biome_tiles(width, height)
|
||||||
# self.tiles: list[list[Tile]] = TileGenerator.generate_random_tiles(width, height)
|
|
||||||
self.image = Image('atlas.png')
|
self.image = Image('atlas.png')
|
||||||
|
|
||||||
def draw(self, camera, visible_area):
|
def draw(self, camera, visible_area):
|
@ -1,15 +1,16 @@
|
|||||||
import pygame.font
|
import pygame.font
|
||||||
|
|
||||||
from survival import settings
|
from survival.ai.genetic_algorithm import GeneticAlgorithm
|
||||||
|
from survival.settings import MUTATE_NETWORKS, SCREEN_HEIGHT, SCREEN_WIDTH
|
||||||
from survival.components.inventory_component import InventoryComponent
|
from survival.components.inventory_component import InventoryComponent
|
||||||
from survival.generators.resource_type import ResourceType
|
from survival.generators.resource_type import ResourceType
|
||||||
from survival.image import Image
|
from survival.game.image import Image
|
||||||
|
|
||||||
|
|
||||||
class UserInterface:
|
class UserInterface:
|
||||||
def __init__(self, window):
|
def __init__(self, window):
|
||||||
self.width = settings.SCREEN_WIDTH
|
self.width = SCREEN_WIDTH
|
||||||
self.height = settings.SCREEN_HEIGHT
|
self.height = SCREEN_HEIGHT
|
||||||
self.window = window
|
self.window = window
|
||||||
self.pos = (self.width - 240, 50)
|
self.pos = (self.width - 240, 50)
|
||||||
self.scale = 2
|
self.scale = 2
|
||||||
@ -25,9 +26,11 @@ class UserInterface:
|
|||||||
i += 1
|
i += 1
|
||||||
self.slot_image = Image('ui.png', self.pos, scale=2)
|
self.slot_image = Image('ui.png', self.pos, scale=2)
|
||||||
self.font = pygame.font.SysFont('Comic Sans MS', 20)
|
self.font = pygame.font.SysFont('Comic Sans MS', 20)
|
||||||
|
self.initialized = False
|
||||||
|
|
||||||
def load_inventory(self, inventory: InventoryComponent):
|
def load_inventory(self, inventory: InventoryComponent):
|
||||||
self.inventory = inventory
|
self.inventory = inventory
|
||||||
|
self.initialized = True
|
||||||
|
|
||||||
def update(self):
|
def update(self):
|
||||||
pass
|
pass
|
||||||
@ -42,4 +45,3 @@ class UserInterface:
|
|||||||
|
|
||||||
textsurface = self.font.render(str(items_count), False, (255, 255, 255))
|
textsurface = self.font.render(str(items_count), False, (255, 255, 255))
|
||||||
self.window.blit(textsurface, (image.pos[0] + 48, image.pos[1] + 36))
|
self.window.blit(textsurface, (image.pos[0] + 48, image.pos[1] + 36))
|
||||||
|
|
@ -1,4 +1,4 @@
|
|||||||
from survival.components.OnCollisionComponent import OnCollisionComponent
|
from survival.components.on_collision_component import OnCollisionComponent
|
||||||
from survival.components.camera_target_component import CameraTargetComponent
|
from survival.components.camera_target_component import CameraTargetComponent
|
||||||
from survival.components.consumption_component import ConsumptionComponent
|
from survival.components.consumption_component import ConsumptionComponent
|
||||||
from survival.components.input_component import InputComponent
|
from survival.components.input_component import InputComponent
|
||||||
@ -25,7 +25,7 @@ class PlayerGenerator:
|
|||||||
inv = InventoryComponent()
|
inv = InventoryComponent()
|
||||||
for resource in ResourceType:
|
for resource in ResourceType:
|
||||||
inv.add_item(resource, STARTING_RESOURCES_AMOUNT)
|
inv.add_item(resource, STARTING_RESOURCES_AMOUNT)
|
||||||
world.add_component(player, ConsumptionComponent(inv.total_items_count()))
|
world.add_component(player, ConsumptionComponent())
|
||||||
world.add_component(player, inv)
|
world.add_component(player, inv)
|
||||||
camera_target = CameraTargetComponent(pos)
|
camera_target = CameraTargetComponent(pos)
|
||||||
world.add_component(player, camera_target)
|
world.add_component(player, camera_target)
|
||||||
|
@ -1,7 +1,7 @@
|
|||||||
import random
|
import random
|
||||||
|
|
||||||
from survival import GameMap
|
from survival import GameMap
|
||||||
from survival.components.OnCollisionComponent import OnCollisionComponent
|
from survival.components.on_collision_component import OnCollisionComponent
|
||||||
from survival.components.inventory_component import InventoryComponent
|
from survival.components.inventory_component import InventoryComponent
|
||||||
from survival.components.learning_component import LearningComponent
|
from survival.components.learning_component import LearningComponent
|
||||||
from survival.components.position_component import PositionComponent
|
from survival.components.position_component import PositionComponent
|
||||||
@ -35,7 +35,7 @@ class ResourceGenerator:
|
|||||||
resource_type = random.choice(list(ResourceType))
|
resource_type = random.choice(list(ResourceType))
|
||||||
sprite = SpriteComponent(sprites[resource_type])
|
sprite = SpriteComponent(sprites[resource_type])
|
||||||
col = OnCollisionComponent()
|
col = OnCollisionComponent()
|
||||||
col.addCallback(self.remove_resource, world=self.world, game_map=self.map, resource_ent=obj, player=player)
|
col.add_callback(self.remove_resource, world=self.world, game_map=self.map, resource_ent=obj, player=player)
|
||||||
self.world.add_component(obj, pos)
|
self.world.add_component(obj, pos)
|
||||||
self.world.add_component(obj, sprite)
|
self.world.add_component(obj, sprite)
|
||||||
self.world.add_component(obj, col)
|
self.world.add_component(obj, col)
|
||||||
|
@ -3,10 +3,10 @@ import random
|
|||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
from typing import List
|
from typing import List
|
||||||
|
|
||||||
from survival.biomes.biome_data import BiomeData
|
from survival.game.biomes.biome_data import BiomeData
|
||||||
from survival.biomes.biome_preset import BiomePreset
|
from survival.game.biomes.biome_preset import BiomePreset
|
||||||
from survival.biomes.noise import generate_noise
|
from survival.game.biomes.noise import generate_noise
|
||||||
from survival.tile import Tile
|
from survival.game.tile import Tile
|
||||||
|
|
||||||
|
|
||||||
class TileGenerator:
|
class TileGenerator:
|
||||||
@ -46,7 +46,8 @@ class TileGenerator:
|
|||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def generate_biome_tiles(width: int, height: int):
|
def generate_biome_tiles(width: int, height: int):
|
||||||
seed = random.randint(1, 10)
|
# Use static seed to allow smooth learning of genetic algorithm
|
||||||
|
seed = 1
|
||||||
octaves = 10
|
octaves = 10
|
||||||
file_name = f'seeds/{seed}.bin'
|
file_name = f'seeds/{seed}.bin'
|
||||||
biomes_file = Path(file_name)
|
biomes_file = Path(file_name)
|
||||||
|
@ -1,5 +1,7 @@
|
|||||||
from survival import esper, PlayerGenerator, ResourceGenerator, SCREEN_WIDTH, SCREEN_HEIGHT, GameMap, \
|
from pathlib import Path
|
||||||
Camera
|
|
||||||
|
from survival import esper, ResourceGenerator, PlayerGenerator
|
||||||
|
from survival.ai.model import LinearQNetwork
|
||||||
from survival.components.consumption_component import ConsumptionComponent
|
from survival.components.consumption_component import ConsumptionComponent
|
||||||
from survival.components.direction_component import DirectionChangeComponent
|
from survival.components.direction_component import DirectionChangeComponent
|
||||||
from survival.components.inventory_component import InventoryComponent
|
from survival.components.inventory_component import InventoryComponent
|
||||||
@ -10,8 +12,11 @@ from survival.components.position_component import PositionComponent
|
|||||||
from survival.components.resource_component import ResourceComponent
|
from survival.components.resource_component import ResourceComponent
|
||||||
from survival.components.time_component import TimeComponent
|
from survival.components.time_component import TimeComponent
|
||||||
from survival.esper import World
|
from survival.esper import World
|
||||||
|
from survival.game.camera import Camera
|
||||||
|
from survival.game.game_map import GameMap
|
||||||
from survival.generators.resource_type import ResourceType
|
from survival.generators.resource_type import ResourceType
|
||||||
from survival.settings import PLAYER_START_POSITION, STARTING_RESOURCES_AMOUNT
|
from survival.settings import PLAYER_START_POSITION, STARTING_RESOURCES_AMOUNT, SCREEN_WIDTH, SCREEN_HEIGHT, \
|
||||||
|
MUTATE_NETWORKS, NETWORK_PARAMS, NEURAL_OUTPUT_SIZE, NEURAL_INPUT_SIZE
|
||||||
from survival.systems.automation_system import AutomationSystem
|
from survival.systems.automation_system import AutomationSystem
|
||||||
from survival.systems.camera_system import CameraSystem
|
from survival.systems.camera_system import CameraSystem
|
||||||
from survival.systems.collision_system import CollisionSystem
|
from survival.systems.collision_system import CollisionSystem
|
||||||
@ -29,7 +34,7 @@ class WorldGenerator:
|
|||||||
def __init__(self, win, callback):
|
def __init__(self, win, callback):
|
||||||
self.win = win
|
self.win = win
|
||||||
self.callback = callback
|
self.callback = callback
|
||||||
self.world: World = esper.World()
|
self.world: World = esper.World(timed=True)
|
||||||
self.game_map: GameMap = GameMap(int(SCREEN_WIDTH / 32) * 2, 2 * int(SCREEN_HEIGHT / 32) + 1)
|
self.game_map: GameMap = GameMap(int(SCREEN_WIDTH / 32) * 2, 2 * int(SCREEN_HEIGHT / 32) + 1)
|
||||||
self.camera = Camera(self.game_map.width * 32, self.game_map.height * 32, self.win)
|
self.camera = Camera(self.game_map.width * 32, self.game_map.height * 32, self.win)
|
||||||
self.resource_generator: ResourceGenerator = ResourceGenerator(self.world, self.game_map)
|
self.resource_generator: ResourceGenerator = ResourceGenerator(self.world, self.game_map)
|
||||||
@ -41,6 +46,14 @@ class WorldGenerator:
|
|||||||
self.world.add_processor(MovementSystem(self.game_map), priority=20)
|
self.world.add_processor(MovementSystem(self.game_map), priority=20)
|
||||||
self.world.add_processor(CollisionSystem(self.game_map), priority=30)
|
self.world.add_processor(CollisionSystem(self.game_map), priority=30)
|
||||||
self.world.add_processor(NeuralSystem(self.game_map, self.callback), priority=50)
|
self.world.add_processor(NeuralSystem(self.game_map, self.callback), priority=50)
|
||||||
|
if not MUTATE_NETWORKS:
|
||||||
|
model_path = Path("/model/model.pth")
|
||||||
|
if model_path.is_file():
|
||||||
|
self.world.get_processor(NeuralSystem).load_model(
|
||||||
|
LinearQNetwork.load(NETWORK_PARAMS, NEURAL_INPUT_SIZE, NEURAL_OUTPUT_SIZE))
|
||||||
|
else:
|
||||||
|
self.world.get_processor(NeuralSystem).load_model(
|
||||||
|
LinearQNetwork(NETWORK_PARAMS, NEURAL_INPUT_SIZE, NEURAL_OUTPUT_SIZE, False, NETWORK_PARAMS))
|
||||||
self.world.add_processor(DrawSystem(self.camera))
|
self.world.add_processor(DrawSystem(self.camera))
|
||||||
self.world.add_processor(TimeSystem())
|
self.world.add_processor(TimeSystem())
|
||||||
self.world.add_processor(AutomationSystem(self.game_map))
|
self.world.add_processor(AutomationSystem(self.game_map))
|
||||||
@ -83,7 +96,7 @@ class WorldGenerator:
|
|||||||
|
|
||||||
if self.world.has_component(self.player, ConsumptionComponent):
|
if self.world.has_component(self.player, ConsumptionComponent):
|
||||||
self.world.remove_component(self.player, ConsumptionComponent)
|
self.world.remove_component(self.player, ConsumptionComponent)
|
||||||
self.world.add_component(self.player, ConsumptionComponent(inv.total_items_count()))
|
self.world.add_component(self.player, ConsumptionComponent())
|
||||||
|
|
||||||
pos = self.world.component_for_entity(self.player, PositionComponent)
|
pos = self.world.component_for_entity(self.player, PositionComponent)
|
||||||
old_pos = pos.grid_position
|
old_pos = pos.grid_position
|
||||||
|
@ -1,78 +0,0 @@
|
|||||||
from random import randint
|
|
||||||
|
|
||||||
import pygame
|
|
||||||
|
|
||||||
|
|
||||||
class Player:
|
|
||||||
def __init__(self):
|
|
||||||
# self.pos = [1024, 512]
|
|
||||||
# self.velocity = [0, 0]
|
|
||||||
# self.image = Image('stevenson.png')
|
|
||||||
# self.image.set_scale(2)
|
|
||||||
# self.speed = 30
|
|
||||||
# self.movement_target = [self.pos[0], self.pos[1]]
|
|
||||||
# self.timer = 0
|
|
||||||
pass
|
|
||||||
|
|
||||||
def draw(self, camera):
|
|
||||||
|
|
||||||
self.image.pos = self.pos
|
|
||||||
camera.draw(self.image)
|
|
||||||
|
|
||||||
def is_moving(self):
|
|
||||||
return self.pos != self.movement_target
|
|
||||||
|
|
||||||
def move_in_random_direction(self):
|
|
||||||
value = randint(0, 3)
|
|
||||||
random_movement = {
|
|
||||||
0: self.move_up,
|
|
||||||
1: self.move_down,
|
|
||||||
2: self.move_left,
|
|
||||||
3: self.move_right
|
|
||||||
}
|
|
||||||
random_movement[value]()
|
|
||||||
|
|
||||||
def update(self, delta, pressed_keys):
|
|
||||||
if self.is_moving():
|
|
||||||
if self.velocity[0] != 0:
|
|
||||||
self.pos[0] += self.velocity[0] * self.speed * delta / 100
|
|
||||||
if abs(self.movement_target[0] - self.pos[0]) < 0.1 * self.speed:
|
|
||||||
self.velocity = [0, 0]
|
|
||||||
self.pos = self.movement_target
|
|
||||||
else:
|
|
||||||
self.pos[1] += self.velocity[1] * self.speed * delta / 100
|
|
||||||
if abs(self.pos[1] - self.movement_target[1]) < 0.1 * self.speed:
|
|
||||||
self.velocity = [0, 0]
|
|
||||||
self.pos = self.movement_target
|
|
||||||
return
|
|
||||||
|
|
||||||
self.timer += delta
|
|
||||||
|
|
||||||
if self.timer > 1000:
|
|
||||||
self.move_in_random_direction()
|
|
||||||
self.timer = 0
|
|
||||||
|
|
||||||
if pressed_keys[pygame.K_LEFT]:
|
|
||||||
self.move_left()
|
|
||||||
elif pressed_keys[pygame.K_RIGHT]:
|
|
||||||
self.move_right()
|
|
||||||
elif pressed_keys[pygame.K_DOWN]:
|
|
||||||
self.move_down()
|
|
||||||
elif pressed_keys[pygame.K_UP]:
|
|
||||||
self.move_up()
|
|
||||||
|
|
||||||
def move_left(self):
|
|
||||||
self.velocity = [-1, 0]
|
|
||||||
self.movement_target = [self.pos[0] - 32, self.pos[1]]
|
|
||||||
|
|
||||||
def move_right(self):
|
|
||||||
self.velocity = [1, 0]
|
|
||||||
self.movement_target = [self.pos[0] + 32, self.pos[1]]
|
|
||||||
|
|
||||||
def move_up(self):
|
|
||||||
self.velocity = [0, -1]
|
|
||||||
self.movement_target = [self.pos[0], self.pos[1] - 32]
|
|
||||||
|
|
||||||
def move_down(self):
|
|
||||||
self.velocity = [0, 1]
|
|
||||||
self.movement_target = [self.pos[0], self.pos[1] + 32]
|
|
@ -1,7 +1,18 @@
|
|||||||
SCREEN_WIDTH = 1000
|
SCREEN_WIDTH = 1000
|
||||||
SCREEN_HEIGHT = 600
|
SCREEN_HEIGHT = 600
|
||||||
RESOURCES_AMOUNT = 100
|
RESOURCES_AMOUNT = 175
|
||||||
DIRECTION_CHANGE_DELAY = 5
|
DIRECTION_CHANGE_DELAY = 5
|
||||||
PLAYER_START_POSITION = [20, 10]
|
PLAYER_START_POSITION = [20, 10]
|
||||||
STARTING_RESOURCES_AMOUNT = 10
|
STARTING_RESOURCES_AMOUNT = 5
|
||||||
AGENT_VISION_RANGE = 5
|
AGENT_VISION_RANGE = 5
|
||||||
|
NEURAL_INPUT_SIZE = 11
|
||||||
|
NEURAL_OUTPUT_SIZE = 3
|
||||||
|
LEARN = True
|
||||||
|
MUTATE_NETWORKS = True
|
||||||
|
NETWORK_PARAMS = {
|
||||||
|
"neurons": 256,
|
||||||
|
"layers": 1,
|
||||||
|
"activation": 'relu',
|
||||||
|
"ratio": 0.001,
|
||||||
|
"optimizer": 'Adam'
|
||||||
|
}
|
||||||
|
@ -7,8 +7,6 @@ from survival.components.resource_component import ResourceComponent
|
|||||||
|
|
||||||
class AutomationComponent:
|
class AutomationComponent:
|
||||||
pass
|
pass
|
||||||
# def __init__(self):
|
|
||||||
# self.resources = []
|
|
||||||
|
|
||||||
|
|
||||||
class AutomationSystem(esper.Processor):
|
class AutomationSystem(esper.Processor):
|
||||||
|
@ -1,11 +1,11 @@
|
|||||||
import operator
|
import operator
|
||||||
|
|
||||||
from survival import esper
|
from survival import esper
|
||||||
from survival.components.OnCollisionComponent import OnCollisionComponent
|
from survival.components.on_collision_component import OnCollisionComponent
|
||||||
from survival.components.moving_component import MovingComponent
|
from survival.components.moving_component import MovingComponent
|
||||||
from survival.components.position_component import PositionComponent
|
from survival.components.position_component import PositionComponent
|
||||||
from survival.components.learning_component import LearningComponent
|
from survival.game.enums import Direction
|
||||||
from survival.enums import Direction
|
from survival.systems.consumption_system import ConsumeComponent
|
||||||
|
|
||||||
|
|
||||||
class CollisionSystem(esper.Processor):
|
class CollisionSystem(esper.Processor):
|
||||||
@ -23,18 +23,20 @@ class CollisionSystem(esper.Processor):
|
|||||||
moving.target = tuple(map(operator.add, vector, pos.grid_position))
|
moving.target = tuple(map(operator.add, vector, pos.grid_position))
|
||||||
moving.direction_vector = vector
|
moving.direction_vector = vector
|
||||||
if self.check_collision(moving.target):
|
if self.check_collision(moving.target):
|
||||||
|
self.world.add_component(ent, ConsumeComponent(0.05))
|
||||||
self.world.remove_component(ent, MovingComponent)
|
self.world.remove_component(ent, MovingComponent)
|
||||||
onCol.callAll()
|
onCol.call_all()
|
||||||
colliding_object: int = self.map.get_entity(moving.target)
|
colliding_object: int = self.map.get_entity(moving.target)
|
||||||
|
|
||||||
if colliding_object is None or not self.world.entity_exists(colliding_object):
|
if colliding_object is None or not self.world.entity_exists(colliding_object):
|
||||||
continue
|
continue
|
||||||
|
|
||||||
if self.world.has_component(colliding_object, OnCollisionComponent):
|
if self.world.has_component(colliding_object, OnCollisionComponent):
|
||||||
self.world.component_for_entity(colliding_object, OnCollisionComponent).callAll()
|
self.world.component_for_entity(colliding_object, OnCollisionComponent).call_all()
|
||||||
|
|
||||||
else:
|
else:
|
||||||
self.map.move_entity(pos.grid_position, moving.target)
|
self.map.move_entity(pos.grid_position, moving.target)
|
||||||
|
self.world.add_component(ent, ConsumeComponent(self.map.get_cost(moving.target)))
|
||||||
pos.grid_position = moving.target
|
pos.grid_position = moving.target
|
||||||
|
|
||||||
def check_collision(self, pos):
|
def check_collision(self, pos):
|
||||||
|
@ -1,30 +1,64 @@
|
|||||||
|
import random
|
||||||
|
|
||||||
from survival import esper
|
from survival import esper
|
||||||
from survival.components.consumption_component import ConsumptionComponent
|
from survival.components.consumption_component import ConsumptionComponent
|
||||||
from survival.components.inventory_component import InventoryComponent
|
from survival.components.inventory_component import InventoryComponent
|
||||||
from survival.components.learning_component import LearningComponent
|
from survival.components.learning_component import LearningComponent
|
||||||
|
from survival.components.moving_component import MovingComponent
|
||||||
from survival.generators.resource_type import ResourceType
|
from survival.generators.resource_type import ResourceType
|
||||||
|
|
||||||
|
|
||||||
|
class ConsumeComponent:
|
||||||
|
def __init__(self, cost):
|
||||||
|
self.cost = cost
|
||||||
|
|
||||||
|
|
||||||
class ConsumptionSystem(esper.Processor):
|
class ConsumptionSystem(esper.Processor):
|
||||||
|
CONSUMPTION_FACTOR = 0.05
|
||||||
|
CONSUMPTION_RANGE = 0.07
|
||||||
|
|
||||||
def __init__(self, callback):
|
def __init__(self, callback):
|
||||||
self.callback = callback
|
self.callback = callback
|
||||||
|
|
||||||
def process(self, dt):
|
def process(self, dt):
|
||||||
for ent, (cons, inventory) in self.world.get_components(ConsumptionComponent, InventoryComponent):
|
cons: ConsumptionComponent
|
||||||
cons.timer -= dt
|
inventory: InventoryComponent
|
||||||
if cons.timer > 0:
|
c: ConsumeComponent
|
||||||
continue
|
for ent, (cons, inventory, c) in self.world.get_components(ConsumptionComponent, InventoryComponent,
|
||||||
cons.timer = cons.timer_value
|
ConsumeComponent):
|
||||||
|
for resource in cons.status.keys():
|
||||||
|
cons.status[resource] -= c.cost * self.CONSUMPTION_FACTOR + random.uniform(-self.CONSUMPTION_RANGE,
|
||||||
|
self.CONSUMPTION_RANGE)
|
||||||
|
if cons.status[resource] < 0:
|
||||||
|
inventory.items[resource] -= 1
|
||||||
|
cons.status[resource] = 1
|
||||||
|
|
||||||
if self.world.has_component(ent, LearningComponent):
|
if self.world.has_component(ent, LearningComponent):
|
||||||
# If no item was picked up
|
for resource in cons.status.keys():
|
||||||
if cons.last_inventory_state == inventory.total_items_count():
|
if inventory.items[resource] <= 0 and self.world.has_component(ent, LearningComponent):
|
||||||
|
# If entity has run out of items
|
||||||
learning: LearningComponent = self.world.component_for_entity(ent, LearningComponent)
|
learning: LearningComponent = self.world.component_for_entity(ent, LearningComponent)
|
||||||
learning.reward += -10
|
learning.reward -= 1
|
||||||
learning.done = True
|
learning.done = True
|
||||||
cons.last_inventory_state = inventory.total_items_count()
|
break
|
||||||
else:
|
else:
|
||||||
if inventory.has_item(ResourceType.FOOD):
|
self.callback(ent)
|
||||||
inventory.remove_item(ResourceType.FOOD, 1)
|
|
||||||
else:
|
self.world.remove_component(ent, ConsumeComponent)
|
||||||
self.callback()
|
# cons.timer -= dt
|
||||||
|
# if cons.timer > 0:
|
||||||
|
# continue
|
||||||
|
# cons.timer = cons.timer_value
|
||||||
|
#
|
||||||
|
# if self.world.has_component(ent, LearningComponent):
|
||||||
|
# # If no item was picked up
|
||||||
|
# if cons.last_inventory_state == inventory.total_items_count():
|
||||||
|
# learning: LearningComponent = self.world.component_for_entity(ent, LearningComponent)
|
||||||
|
# learning.reward += -10
|
||||||
|
# learning.done = True
|
||||||
|
# cons.last_inventory_state = inventory.total_items_count()
|
||||||
|
# else:
|
||||||
|
# if inventory.has_item(ResourceType.FOOD):
|
||||||
|
# inventory.remove_item(ResourceType.FOOD, 1)
|
||||||
|
# else:
|
||||||
|
# self.callback()
|
||||||
|
@ -1,7 +1,7 @@
|
|||||||
from survival import esper
|
from survival import esper
|
||||||
from survival.components.position_component import PositionComponent
|
from survival.components.position_component import PositionComponent
|
||||||
from survival.components.sprite_component import SpriteComponent
|
from survival.components.sprite_component import SpriteComponent
|
||||||
from survival.user_interface import UserInterface
|
from survival.game.user_interface import UserInterface
|
||||||
|
|
||||||
|
|
||||||
class DrawSystem(esper.Processor):
|
class DrawSystem(esper.Processor):
|
||||||
@ -17,5 +17,6 @@ class DrawSystem(esper.Processor):
|
|||||||
sprite.image.pos = pos.position
|
sprite.image.pos = pos.position
|
||||||
sprite.image.origin = (32 * pos.direction.value, 0)
|
sprite.image.origin = (32 * pos.direction.value, 0)
|
||||||
self.camera.draw(sprite.image)
|
self.camera.draw(sprite.image)
|
||||||
|
if self.ui.initialized:
|
||||||
self.ui.update()
|
self.ui.update()
|
||||||
self.ui.draw()
|
self.ui.draw()
|
||||||
|
@ -4,20 +4,20 @@ from collections import deque
|
|||||||
import torch
|
import torch
|
||||||
|
|
||||||
from survival import esper, GameMap
|
from survival import esper, GameMap
|
||||||
|
from survival.ai.genetic_algorithm import GeneticAlgorithm
|
||||||
from survival.components.direction_component import DirectionChangeComponent
|
from survival.components.direction_component import DirectionChangeComponent
|
||||||
from survival.components.inventory_component import InventoryComponent
|
from survival.components.inventory_component import InventoryComponent
|
||||||
from survival.components.moving_component import MovingComponent
|
from survival.components.moving_component import MovingComponent
|
||||||
from survival.components.position_component import PositionComponent
|
from survival.components.position_component import PositionComponent
|
||||||
from survival.components.learning_component import LearningComponent
|
from survival.components.learning_component import LearningComponent
|
||||||
from survival.components.time_component import TimeComponent
|
from survival.components.time_component import TimeComponent
|
||||||
from survival.graph_search import Action
|
from survival.ai.graph_search import Action
|
||||||
from survival.learning_utils import get_state, LearningUtils
|
from survival.ai.learning_utils import get_state, LearningUtils
|
||||||
from survival.model import LinearQNetwork, QTrainer
|
from survival.ai.model import LinearQNetwork, QTrainer
|
||||||
|
from survival.settings import LEARN, MUTATE_NETWORKS
|
||||||
|
|
||||||
MAX_MEMORY = 100_000
|
MAX_MEMORY = 100_000
|
||||||
BATCH_SIZE = 1000
|
BATCH_SIZE = 1000
|
||||||
LR = 0.001
|
|
||||||
LEARN = False
|
|
||||||
|
|
||||||
|
|
||||||
class NeuralSystem(esper.Processor):
|
class NeuralSystem(esper.Processor):
|
||||||
@ -25,17 +25,27 @@ class NeuralSystem(esper.Processor):
|
|||||||
self.game_map = game_map
|
self.game_map = game_map
|
||||||
self.reset_game = callback
|
self.reset_game = callback
|
||||||
self.n_games = 0 # number of games played
|
self.n_games = 0 # number of games played
|
||||||
|
if MUTATE_NETWORKS:
|
||||||
|
self.starting_epsilon = GeneticAlgorithm.GAMES_PER_NETWORK / 2
|
||||||
|
else:
|
||||||
self.starting_epsilon = 100
|
self.starting_epsilon = 100
|
||||||
self.epsilon = 0 # controlls the randomness
|
self.epsilon = 0 # controlls the randomness
|
||||||
self.gamma = 0.9 # discount rate
|
self.gamma = 0.9 # discount rate
|
||||||
self.memory = deque(maxlen=MAX_MEMORY) # exceeding memory removes the left elements to make more space
|
self.memory = deque(maxlen=MAX_MEMORY) # exceeding memory removes the left elements to make more space
|
||||||
self.model = LinearQNetwork.load(11, 256, 3)
|
self.model = None # self.model = LinearQNetwork.load(11, 256, 3)
|
||||||
if self.model.pretrained:
|
self.trainer = None # QTrainer(self.model, lr=LR, gamma=self.gamma)
|
||||||
self.starting_epsilon = -1
|
|
||||||
self.trainer = QTrainer(self.model, lr=LR, gamma=self.gamma)
|
|
||||||
self.utils = LearningUtils()
|
self.utils = LearningUtils()
|
||||||
self.best_action = None
|
self.best_action = None
|
||||||
|
|
||||||
|
def load_model(self, model: LinearQNetwork):
|
||||||
|
self.model = model
|
||||||
|
self.trainer = QTrainer(self.model, self.model.network_params['ratio'], self.gamma,
|
||||||
|
self.model.network_params['optimizer'])
|
||||||
|
self.utils = LearningUtils()
|
||||||
|
self.memory = deque(maxlen=MAX_MEMORY)
|
||||||
|
self.starting_epsilon = GeneticAlgorithm.GAMES_PER_NETWORK / 2
|
||||||
|
self.n_games = 0
|
||||||
|
|
||||||
def remember(self, state, action, reward, next_state, done):
|
def remember(self, state, action, reward, next_state, done):
|
||||||
self.memory.append((state, action, reward, next_state, done))
|
self.memory.append((state, action, reward, next_state, done))
|
||||||
|
|
||||||
@ -119,11 +129,13 @@ class NeuralSystem(esper.Processor):
|
|||||||
self.train_long_memory()
|
self.train_long_memory()
|
||||||
if learning.score > learning.record:
|
if learning.score > learning.record:
|
||||||
learning.record = learning.score
|
learning.record = learning.score
|
||||||
if LEARN:
|
if LEARN and not MUTATE_NETWORKS:
|
||||||
self.model.save()
|
self.model.save()
|
||||||
|
|
||||||
print('Game', self.n_games, 'Score', learning.score, 'Record', learning.record)
|
# print('Game', self.n_games, 'Score', learning.score, 'Record', learning.record)
|
||||||
self.utils.add_scores(learning, self.n_games)
|
self.utils.add_scores(learning, self.n_games)
|
||||||
|
|
||||||
|
self.model.scores.append(learning.score)
|
||||||
learning.score = 0
|
learning.score = 0
|
||||||
self.utils.plot()
|
self.utils.plot()
|
||||||
|
|
||||||
|
@ -3,8 +3,8 @@ from survival.components.direction_component import DirectionChangeComponent
|
|||||||
from survival.components.movement_component import MovementComponent
|
from survival.components.movement_component import MovementComponent
|
||||||
from survival.components.moving_component import MovingComponent
|
from survival.components.moving_component import MovingComponent
|
||||||
from survival.components.position_component import PositionComponent
|
from survival.components.position_component import PositionComponent
|
||||||
from survival.enums import Direction
|
from survival.game.enums import Direction
|
||||||
from survival.graph_search import graph_search, Action
|
from survival.ai.graph_search import graph_search, Action
|
||||||
from survival.systems.input_system import PathfindingComponent
|
from survival.systems.input_system import PathfindingComponent
|
||||||
|
|
||||||
|
|
||||||
|
Loading…
Reference in New Issue
Block a user