def startPrey():
prey_robot = robobo.SimulationRoboboPrey().connect(address='127.0.0.1',
port=19989)
prey_controller = prey.Prey(robot=prey_robot, level=0)
prey_controller.start()
return (prey_controller, prey_robot)
def main():
signal.signal(signal.SIGINT, terminate_program)
rob = robobo.SimulationRobobo().connect(address='127.0.0.1', port=19997)
rob.play_simulation()
prey_robot = robobo.SimulationRoboboPrey().connect(address='127.0.0.1',
port=19989)
prey_controller = prey.Prey(robot=prey_robot, level=2)
prey_controller.start()
for i in range(10):
print("robobo is at {}".format(rob.position()))
rob.move(5, 5, 2000)
prey_controller.stop()
prey_controller.join()
prey_robot.disconnect()
rob.stop_world()
# time.sleep(10)
# rob.kill_connections()
# rob = robobo.SimulationRobobo().connect(address='192.168.1.6', port=19997)
rob.play_simulation()
# prey_robot = robobo.SimulationRoboboPrey().connect(address='192.168.1.71', port=19989)
# prey_controller = prey.Prey(robot=prey_robot, level=2, hardware=True)
prey_robot = robobo.SimulationRoboboPrey().connect(address='127.0.0.1',
port=19989)
prey_controller = prey.Prey(robot=prey_robot, level=2)
prey_controller.start()
for i in range(10):
print("robobo is at {}".format(rob.position()))
rob.move(5, 5, 2000)
prey_controller.stop()
prey_controller.join()
rob.stop_world()
def __init__(self, initialPredatorPopulation, initialPreyPopulation):
pygame.init()
self.clock = pygame.time.Clock()
self.surface = pygame.display.set_mode((WIDTH, HEIGHT), 0, 32)
self.predators = []
self.prey = []
self.food = []
for i in range(initialPredatorPopulation):
p = predator.Predator(
(
predator.MAX_HEALTH,
predator.VIEW_RADIUS,
predator.MAX_VELOCITY,
(random.randint(0, WIDTH), random.randint(0, HEIGHT)),
(random.uniform(-1, 1) * INIT_VELOCITY, random.uniform(-1, 1) * INIT_VELOCITY),
predator.COLOR,
predator.SIZE,
),
predator.MAX_DETECTION_OF_PREY * random.uniform(-1, 1),
predator.MAX_ATTRACTION_TO_PREY * random.uniform(-1, 1),
)
self.predators.append(p)
for i in range(initialPreyPopulation):
p = prey.Prey(
(
prey.MAX_HEALTH,
prey.VIEW_RADIUS,
prey.MAX_VELOCITY,
(random.randint(0, WIDTH), random.randint(0, HEIGHT)),
(random.uniform(-1, 1) * INIT_VELOCITY, random.uniform(-1, 1) * INIT_VELOCITY),
prey.COLOR,
prey.SIZE,
),
prey.MAX_DETECTION_OF_FOOD * random.uniform(-1, 1),
prey.MAX_ATTRACTION_TO_FOOD * random.uniform(-1, 1),
prey.MAX_DETECTION_OF_PREDATOR * random.uniform(-1, 1),
prey.MAX_REPULSION_FROM_PREDATOR * random.uniform(-1, 1),
)
self.prey.append(p)
for i in range(MAX_FOOD_SUPPLY):
f = food.Food(
(random.randint(0, WIDTH), random.randint(0, HEIGHT)),
food.COLOR,
food.SIZE
)
self.food.append(f)
pygame.display.set_caption('SIMULATOR')
def rl(alpha,
gamma,
epsilon,
episodes,
act_lim,
param_tuples,
qL=False,
no_blocks=0):
fitness = list()
rewards = [0]
for i in range(episodes):
print('Episode ' + str(i))
terminate = False
if use_simulation:
rob.play_simulation()
prey_robot = robobo.SimulationRoboboPrey().connect(
address='172.17.0.1', port=19989)
else:
prey_robot = robobo.HardwareRobobo(camera=True).connect(
address="10.15.3.48") # Connect to different ip?
prey_controller = prey.Prey(robot=prey_robot, level=2)
prey_controller.start(
) # Not sure yet if this is indeed needed for both simu and hardware
current_color_space = get_color_info()
current_sensor_info = get_sensor_info('front_3')
current_state = make_discrete(current_sensor_info, boundary_sensor,
current_color_space,
boundaries_color)
if str(current_state) not in state_table.keys():
state_table[str(current_state)] = [0 for _ in range(3)]
action = epsilon_policy(current_state, epsilon)
# current_collected_food = rob.collected_food() if use_simulation else 0
# initialise state if it doesn't exist, else retrieve the current q-value
x = 0
while not terminate:
take_action(action)
# new_collected_food = rob.collected_food() if use_simulation else 0
# Whole img extracted to get reward value
# left, mid, right extracted to save state space accordingly
new_color_space = get_color_info()
new_sensor_info = get_sensor_info('front_3')
new_state = make_discrete(new_sensor_info, boundary_sensor,
new_color_space, boundaries_color)
if str(new_state) not in state_table.keys():
state_table[str(new_state)] = [0 for _ in range(3)]
new_action = epsilon_policy(new_state, epsilon)
# Retrieve the max action if we use Q-Learning
max_action = np.argmax(
state_table[str(new_state)]) if qL else new_action
# Get reward
r = get_reward(current_state, new_state, current_sensor_info,
new_sensor_info, action, new_action,
current_color_space, new_color_space)
print("State and obtained Reward: ", new_state, r)
# norm_r = normalize(r, -30, 20)
#
# if i != 0:
# fitness.append(no_blocks / i)
# else:
# fitness.append(float(0))
# # print(fitness)
# if rewards:
# rewards.append(rewards[-1] + norm_r)
# else:
# rewards.append(norm_r)
# Update rule
if not run_test:
# print('update')
state_table[str(current_state)][action] += \
alpha * (r + (gamma *
np.array(
state_table[str(new_state)][max_action]))
- np.array(state_table[str(current_state)][action]))
# Stop episode if we get very close to an obstacle
if (max(new_state[:3]) == 2 and max(new_state[3:]) != 2
and use_simulation) or x == act_lim - 1:
state_table[str(new_state)][new_action] = -10
terminate = True
print("done")
if not run_test:
print('writing json')
with open(q_table_file, 'w') as json_file:
json.dump(state_table, json_file)
if use_simulation:
print("stopping the simulation")
prey_controller.stop()
prey_controller.join()
prey_robot.disconnect()
rob.stop_world()
while not rob.is_sim_stopped():
print("waiting for the simulation to stop")
time.sleep(2)
# update current state and action
current_state = new_state
current_sensor_info = new_sensor_info
action = new_action
current_color_space = new_color_space
# increment action limit counter
x += 1
return fitness, rewards
def main():
signal.signal(signal.SIGINT, terminate_program)
# rob = robobo.HardwareRobobo(camera=True).connect(address="192.168.1.7")
rob = robobo.SimulationRobobo().connect(address='192.168.1.6', port=19997)
rob.play_simulation()
# ALWAYS connect first to the real robot, then start the simulation and only then connect to the prey
# if the order is not respected, an error is raised and I do not why
# if you use the provided scene, do not change the port number
# if you want to build your own scene, remember to modify the prey port number on vrep
prey_robot = robobo.SimulationRoboboPrey().connect(address='192.168.1.6', port=19989)
#
# rob.pause_simulation()
# move and talk
# rob.set_emotion('sad')
# initialise class prey
# needs to receive the robot to move
# there are 5 levels of difficulties. From 0 (super easy) to 4 (hard).
# you can select the one you want,using a parameter in the following constructor, default is 2
prey_controller = prey.Prey(robot=prey_robot, level=2)
# start the thread prey
# makes the prey move
prey_controller.start()
# print("start")
for i in range(10):
print("robobo is at {}".format(rob.position()))
rob.move(5, 5, 2000)
#
# print("robobo is at {}".format(rob.position()))
# rob.sleep(1)
# # Following code moves the phone stand
# rob.set_phone_pan(343, 100)
# rob.set_phone_tilt(109, 100)
# time.sleep(1)
# rob.set_phone_pan(11, 100)
# rob.set_phone_tilt(26, 100)
# rob.talk('Hi, my name is Robobo')
# rob.sleep(1)
# rob.set_emotion('happy')
# Following code gets an image from the camera
# image = rob.get_image_front()
# cv2.imwrite("test_pictures.png",image)
# time.sleep(0.1)
# # IR reading
# for i in range(1000000):
# print("ROB Irs: {}".format(np.log(np.array(rob.read_irs()))/10))
# time.sleep(0.1)
# stop the prey
# if you want to stop the prey you have to use the two following commands
prey_controller.stop()
prey_controller.join()
prey_robot.disconnect()
# pause the simulation and read the collected food
# rob.pause_simulation()
# print("Robobo collected {} food".format(rob.collected_food()))
# Stopping the simualtion resets the environment
rob.stop_world()
time.sleep(10)
# rob.kill_connections()
# rob = robobo.SimulationRobobo().connect(address='192.168.1.6', port=19997)
rob.play_simulation()
# prey_robot = robobo.SimulationRoboboPrey().connect(address='192.168.1.71', port=19989)
# prey_controller = prey.Prey(robot=prey_robot, level=2, hardware=True)
prey_robot = robobo.SimulationRoboboPrey().connect(address='192.168.1.6', port=19989)
prey_controller = prey.Prey(robot=prey_robot, level=2)
prey_controller.start()
for i in range(10):
print("robobo is at {}".format(rob.position()))
rob.move(5, 5, 2000)
prey_controller.stop()
prey_controller.join()
rob.stop_world()
def rl(alpha, gamma, epsilon, episodes, act_lim, exp, qL=False):
fitness = list()
rewards = [0]
for i in range(episodes):
print('Episode ' + str(i))
terminate = False
prey_robot = []
if use_simulation:
rob.play_simulation()
prey_robot = robobo.SimulationRoboboPrey().connect(
address=virtual_ip, port=19989)
if not use_simulation:
prey_robot = robobo.HardwareRobobo().connect(
address=robot_prey)
prey_controller = prey.Prey(
robot=prey_robot, level=2) if use_simulation else prey.Prey(
robot=prey_robot, level=4)
prey_controller.start()
current_color_space = get_color_info()
current_sensor_info = get_sensor_info('front_3')
current_state = make_discrete(current_sensor_info, boundary_sensor,
current_color_space,
boundaries_color)
if str(current_state) not in state_table.keys():
state_table[str(current_state)] = [0 for _ in range(3)]
action = epsilon_policy(current_state, epsilon)
x = 0
while not terminate:
take_action(action)
# new_collected_food = rob.collected_food() if use_simulation else 0
# Whole img extracted to get reward value
# left, mid, right extracted to save state space accordingly
new_color_space = get_color_info()
new_sensor_info = get_sensor_info('front_3')
new_state = make_discrete(new_sensor_info, boundary_sensor,
new_color_space, boundaries_color)
if str(new_state) not in state_table.keys():
state_table[str(new_state)] = [0 for _ in range(3)]
new_action = epsilon_policy(new_state, epsilon)
# Retrieve the max action if we use Q-Learning
max_action = np.argmax(
state_table[str(new_state)]) if qL else new_action
# Get reward
r = get_reward(current_state, new_state, current_sensor_info,
new_sensor_info, action, new_action,
current_color_space, new_color_space)
print("State and obtained Reward: ", new_state, r)
norm_r = normalize(r, MIN_REWARD, MAX_REWARD)
norm_steps = normalize(x, MIN_TIMESTEPS + 0.01, MAX_TIMESTEPS)
fitness.append(norm_r / norm_steps)
# Update rule
if not run_test:
# print('update')
state_table[str(current_state)][action] += \
alpha * (r + (gamma *
np.array(
state_table[str(new_state)][max_action]))
- np.array(state_table[str(current_state)][action]))
# Stop episode if we get very close to an obstacle
if (max(new_state[:3]) == 2 and max(new_state[3:]) != 2
and use_simulation) or x == act_lim - 1:
# state_table[str(new_state)][new_action] = -10
terminate = True
print("done")
if not run_test:
print('writing json')
with open(
'./src/task3_results/state_table_e' +
str(epsilon) + '_exp_' + str(exp) + '.json',
'wb') as json_file:
json.dump(state_table, json_file)
if use_simulation:
print("stopping the simulation")
prey_controller.stop()
prey_controller.join()
prey_robot.disconnect()
rob.stop_world()
while not rob.is_sim_stopped():
print("waiting for the simulation to stop")
time.sleep(2)
# update current state and action
current_state = new_state
current_sensor_info = new_sensor_info
action = new_action
current_color_space = new_color_space
# increment action limit counter
x += 1
return fitness, rewards
def main():
signal.signal(signal.SIGINT, terminate_program)
# rob = robobo.HardwareRobobo(camera=True).connect(address="192.168.1.7")
rob = robobo.SimulationRobobo(number='').connect(address='127.0.0.1',
port=19997)
rob.play_simulation()
rob.set_phone_tilt(0.8, 50)
image = rob.get_image_front()
# mask = cv2.inRange(image, (0, 140, 0), (20, 255, 20))
mask = cv2.inRange(image, (0, 0, 80), (60, 60, 255))
# mask = cv2.bitwise_not(mask, mask)
# masked = cv2.bitwise_and(image, image, mask=mask)
cv2.imshow('9', mask)
cv2.imshow('1', image)
cv2.waitKey(0) # waits until a key is pressed
cv2.destroyAllWindows()
exit()
# prey_robot = robobo.SimulationRoboboPrey().connect(address='192.168.1.71', port=19989)
# prey_controller = prey.Prey(robot=prey_robot, level=2, hardware=True)
prey_robot = robobo.SimulationRoboboPrey('#0').connect(address='127.0.0.1',
port=19989)
prey_controller = prey.Prey(robot=prey_robot, level=2)
prey_controller.start()
for i in range(5000):
print("robobo is at {}".format(rob.position()))
rob.move(90, 90, millis=200)
prey_controller.stop()
prey_controller.join()
prey_robot.disconnect()
rob.stop_world()
# move and talk
# rob.set_emotion('sad')
# initialise class prey
# needs to receive the robot to move
# there are 5 levels of difficulties. From 0 (super easy) to 4 (hard).
# you can select the one you want,using a parameter in the following constructor, default is 2
prey_controller = prey.Prey(robot=prey_robot, level=2)
# start the thread prey
# makes the prey move
prey_controller.start()
# print("start")
for i in range(10):
print("robobo is at {}".format(rob.position()))
rob.move(5, 5, 2000)
#
# print("robobo is at {}".format(rob.position()))
# rob.sleep(1)
# # Following code moves the phone stand
# rob.set_phone_pan(343, 100)
# rob.set_phone_tilt(109, 100)
# time.sleep(1)
# rob.set_phone_pan(11, 100)
# rob.set_phone_tilt(26, 100)
# rob.talk('Hi, my name is Robobo')
# rob.sleep(1)
# rob.set_emotion('happy')
# Following code gets an image from the camera
# image = rob.get_image_front()
# cv2.imwrite("test_pictures.png",image)
# time.sleep(0.1)
# # IR reading
# for i in range(1000000):
# print("ROB Irs: {}".format(np.log(np.array(rob.read_irs()))/10))
# time.sleep(0.1)
# stop the prey
# if you want to stop the prey you have to use the two following commands
prey_controller.stop()
prey_controller.join()
prey_robot.disconnect()
# pause the simulation and read the collected food
# rob.pause_simulation()
# print("Robobo collected {} food".format(rob.collected_food()))
# Stopping the simualtion resets the environment
rob.stop_world()
time.sleep(10)
# rob.kill_connections()
# rob = robobo.SimulationRobobo().connect(address='192.168.1.6', port=19997)
rob.play_simulation()
rob.set_phone_tilt(0.8, 50)
# prey_robot = robobo.SimulationRoboboPrey().connect(address='192.168.1.71', port=19989)
# prey_controller = prey.Prey(robot=prey_robot, level=2, hardware=True)
prey_robot = robobo.SimulationRoboboPrey().connect(address='192.168.1.6',
port=19989)
prey_controller = prey.Prey(robot=prey_robot, level=2)
prey_controller.start()
for i in range(10):
print("robobo is at {}".format(rob.position()))
rob.move(90, 90, millis=200)
prey_controller.stop()
prey_controller.join()
rob.stop_world()
def populate_prey(l, config):
for x in range(config["num_birds"]):
b = prey.Prey(random_position(config), (random.randint(1, 5), random.randint(1, 5)), x, config)
l.append(b)