class Simulation:
def __init__(self):
self.__environment = Environment()
self.__frequency_falsepos = 0
self.__searchagent = SearchAgent()
def __setup_test_environment(self, x=10, y=10):
self.__environment.clear()
self.__environment.generate(x, y)
self.__environment.add_target()
def __setup_search_agent(self, searchagent_genes=list()):
self.__searchagent.set_brain(searchagent_genes)
self.__environment.add_search_agent()
def run_simulation(self, num_of_turns=300):
return_scores = list()
for i in range(num_of_turns):
# agent think and validation of the decision
move_result = self.__environment.move_searchagent(
self.__searchagent.agent_id(),
self.__searchagent.think(
self.__environment.get_adjacent_tiles(
self.__searchagent.agent_id())))
if move_result[1] == True:
# agent move if the move is valid
self.__searchagent.move(move_result[0])
if self.__environment.check_target(
self.__searchagent.agent_id()):
self.__searchagent.search_success()
else:
# if the move is invalid, returns a value far beyond the lowest possible value
self.__searchagent.reset()
return -10000
# self.__environment.draw()
return_scores = grade(self.__searchagent)
self.__environment.clear()
self.__searchagent.reset()
return return_scores
def setup_simulation(self,
x=10,
y=10,
num_search_targets=1,
num_search_agents=1,
searchagent_genes=list()):
self.__setup_test_environment(x, y)
self.__setup_search_agent(searchagent_genes)
self.environment = environment
def run(self):
while(True):
# Movement of targets eq.(2):
if(np.random.random() > 0.9999):
print("Change speed!")
with self.lock:
self.environment.set_targets_command((np.random.random((self.environment.xi.shape))-0.5))
self.environment.update()
time.sleep(self.environment.t)
env = Environment(10,10,0.01, True)
for i in range(5):
env.add_target(np.random.random()*2.5-2.5, np.random.random()*2.5-2.5, 0.005, 0.005)
env.add_agent(np.random.random()*2.5-2.5, np.random.random()*2.5-2.5, 0.005, 0.005)
# env.add_agent(np.random.random()*2.5-2.5, np.random.random()*2.5-2.5, 0.005, 0.005)
main = Main(env)
main.start()
#main.run()
plt.gcf()
plt.show()
# plt.savefig("output/" + "MRS" + str(time.time()) + ".png")
main.join()
# To recover a final video of de execution:
# ffmpeg -f image2 -s 1920x1080 -i output/MRS%5d.png -vcodec libx264 -crf 25 -pix_fmt yuv420p MRS.mp4