def move(self, environment):
if self.model is None:
self.model = DeepNeuralNetModel(Constants.MODEL_DIRECTORY +
"/dnn_genetic_evolution/")
BaseGameModel.move(self, environment)
predicted_action = self._predict(environment, self.model)
return predicted_action
def move(self, environment):
BaseGameModel.move(self, environment)
if self.action is None:
return environment.snake_action
backward_action = self.action[0] == environment.snake_action[
0] * -1 or self.action[1] == environment.snake_action[1] * -1
return environment.snake_action if backward_action else self.action
def move(self, environment):
BaseGameModel.move(self, environment)
shortest_path_move_from_transposition_table = self._path_move_from_transposition_table(
self.starting_node, self.fruit_node)
if shortest_path_move_from_transposition_table:
return shortest_path_move_from_transposition_table
stack = Stack([self.starting_node])
visited_nodes = set([self.starting_node])
shortest_path = []
while stack.stack:
current_node = stack.pop()
if current_node == self.fruit_node:
shortest_path = self._recreate_path_for_node(current_node)
for move in environment.possible_actions_for_current_action(
current_node.action):
child_node_point = Point(current_node.point.x + move[0],
current_node.point.y + move[1])
neighbor = environment.tiles[child_node_point.y][
child_node_point.x]
if neighbor == Tile.empty or neighbor == Tile.fruit:
child_node = Node(child_node_point)
child_node.action = move
child_node.previous_node = current_node
if child_node not in visited_nodes and child_node not in stack.stack:
visited_nodes.add(current_node)
stack.push(child_node)
if shortest_path:
self.transposition_table[self.fruit_node] = shortest_path
first_point = shortest_path[-2]
return first_point.action
return environment.snake_action
def move(self, environment):
BaseGameModel.move(self, environment)
longest_path = self.longest_path(self.starting_node, self.fruit_node, environment)
for index in range(0, len(longest_path)):
node = longest_path[index]
if node == self.starting_node:
return longest_path[index+1].action
return environment.snake_action
def __init__(self, long_name, short_name, abbreviation):
BaseGameModel.__init__(self, long_name, short_name, abbreviation)
self.model_path = self.model_dir_path + Constants.DQN_MODEL_NAME
if os.path.exists(os.path.dirname(self.model_path)):
shutil.rmtree(os.path.dirname(self.model_path), ignore_errors=True)
os.makedirs(os.path.dirname(self.model_path))
self.action_space = len(Action.possible())
self.ddqn = DDQNModel(self.model_input_shape, self.action_space).model
self._load_model()
def move(self, environment):
BaseGameModel.move(self, environment)
shortest_path_move_from_transposition_table = self._path_move_from_transposition_table(
self.starting_node, self.fruit_node)
if shortest_path_move_from_transposition_table:
return shortest_path_move_from_transposition_table
shortest_path = self.shortest_path(environment, self.starting_node,
self.fruit_node)
if shortest_path:
self.transposition_table[self.fruit_node] = shortest_path
first_point = shortest_path[-2]
return first_point.action
return environment.snake_action
def move(self, environment):
BaseGameModel.move(self, environment)
if environment.is_in_fruitless_cycle():
print("Infinite fruitless cycle - game over at: " +
str(environment.reward()))
return environment.snake_action
hamilton_path = self._hamilton_path(environment)
for index in range(0, len(hamilton_path)):
node = hamilton_path[index]
next_index = index + 1
if next_index == len(hamilton_path):
return hamilton_path[0].action
elif node == self.starting_node:
return hamilton_path[next_index].action
return environment.snake_action
def __init__(self):
BaseGameModel.__init__(self, "Shortest Path BFS", "shortest_path_bfs",
"spb")
def move(self, environment):
if self.model is None:
self.model = DeepNeuralNetModel(Constants.MODEL_DIRECTORY + "dnn/")
BaseGameModel.move(self, environment)
def __init__(self, long_name, short_name, abbreviation):
BaseGameModel.__init__(self, long_name, short_name, abbreviation)
def __init__(self):
BaseGameModel.__init__(self, "Shortest Path DFS", "shortest_path_dfs",
"spd")
def __init__(self):
BaseGameModel.__init__(self, "Random", "random", "r")
def __init__(self):
BaseGameModel.__init__(self, "Longest Path", "longest_path", "lp")
def __init__(self):
BaseGameModel.__init__(self, "Human", "human", "hu")
def __init__(self, runs=1000):
BaseGameModel.__init__(self, "Monte Carlo", "monte_carlo", "mc")
self.runs = runs
def move(self, environment):
BaseGameModel.move(self, environment)
def __init__(self):
BaseGameModel.__init__(self, "Deep Neural Net GE",
"deep_neural_net_genetic_evolution_trainer",
"dnnget")
def move(self, environment):
BaseGameModel.move(self, environment)
return random.choice(
environment.possible_actions_for_current_action(
environment.snake_action))
def __init__(self):
BaseGameModel.__init__(self, "Hamilton", "hamilton", "ha")
