def move_scenario():
"""
One apad is added and it should move somewhere.
Figure out how times and speed will be calculated. 1 second
of our time is 30 minutes in simulation, but speed of apad is 0.5 meters per second.
How will we calculate everything??
"""
#deltat = 1 # this is 30 minutes
#deltat = 0.5 # this is 15 minutes
deltat = 0.25 # this is 7.5 minutes
topology = util.parser(
filename=os.path.join("..", "init_files", "init_topo_exmple.txt"))
apad = topology.all_agents[0]
r = rospy.Rate(1 / deltat) # 1 Hz
xs = []
ys = []
while not rospy.is_shutdown():
apad.move(deltat)
x, y = apad.coordinates
xs.append(x)
ys.append(y)
r.sleep()
if len(xs) > 10:
break
util.plot_coordinates(xs, ys)
def test_and_plot(num_of_iterations):
counter_weights = check_pickle_file_path(approximate_weights_path)
players, number_of_games, levels, discount, epsilon, print_mode = parser(
sys.argv)
winning_percentages = [0] * num_of_iterations
game = None
for i in range(num_of_iterations):
print("Start iteration number %d" % (i + 1))
game = GameManager(players,
number_of_games,
levels,
discount,
epsilon,
print_mode=print_mode,
counter_weights_list=counter_weights)
game.run_game() # run the test
game.print_scoring_table()
winning_percentages[i] = game.get_player_score(
0) / number_of_games # insert reinforcement agent index
print("Finish iteration number %d" % (i + 1))
player_1_type = game.get_player_type(0)
player_2_type = game.get_player_type(1)
print("The average %s winning percentage is %6.2f" %
(player_1_type, sum(winning_percentages) / num_of_iterations))
pyplot.plot(range(1, len(winning_percentages) + 1), winning_percentages)
pyplot.xlabel("Iteration number")
pyplot.ylabel("%s winning percentage" % player_1_type)
title = "%s vs %s\n%d games in each iteration" % (
player_1_type, player_2_type, number_of_games)
if player_1_type == "AlphaBetaPruning" or player_2_type == "AlphaBetaPruning":
title += "\nAlpha Beta with depth " + str(levels)
pyplot.title(title)
pyplot.show()
def scenarioGoToNearest():
system = parser(filename="evolutionary_init.txt")
mussel_coordinates = [list(item.coordinates) for item in system.mussels]
start = min(mussel_coordinates,
key=lambda x: distance(list(system.pads[0].coordinates), x))
points = go_to_nearest(system, start)
totalDistance = total_distance(points)
system.plot_topology(
annotate_energy=False,
order_of_passing=True,
title=
"Way of prioritizing: Go to nearest point with nearest first point; Total Distance: {}"
.format(totalDistance))
def run_from_parser():
counter_weights = check_pickle_file_path(approximate_weights_path)
players, number_of_games, levels, discount, epsilon, print_mode = parser(
sys.argv)
game = GameManager(players,
number_of_games,
levels,
discount,
epsilon,
print_mode=print_mode,
counter_weights_list=counter_weights)
game.run_game() # run the test
game.print_scoring_table()
def scenarioEvolutionary():
system = parser(filename="evolutionary_init.txt")
mussel_coordinates = [list(item.coordinates) for item in system.mussels]
bestGene, points_dict = evolutionary.main(mussel_coordinates,
system.pads[0].coordinates)
switchDict = dict()
for key, item in points_dict.items():
switchDict[tuple(item)] = key
# for i, mussel in enumerate(system.mussels):
points = bestGene.points
for i, mussel in enumerate(system.mussels):
system.mussels[i].order_of_passing = points.index(
list(mussel.coordinates))
totalDistance = total_distance(points, system.pads[0].coordinates)
system.plot_topology(
annotate_energy=False,
order_of_passing=True,
title="Way of prioritizing: Evolutionary algorithm; Total Distance: {}"
.format(totalDistance))
plt.legend()
for (i, mussel) in enumerate(self.mussels):
if annotate_energy:
ax.annotate(str(mussel.energy),
(mussels_x[i] + 0.05, mussels_y[i] + 0.05))
if order_of_passing:
ax.annotate(str(mussel.order_of_passing),
(mussels_x[i] - 0.08, mussels_y[i] - 0.08))
for (i, apad) in enumerate(self.pads):
if annotate_energy:
ax.annotate(str(apad.energy),
(pads_x[i] + 0.05, pads_y[i] + 0.05))
plt.grid()
plt.xlabel("X coordinates of agents")
plt.ylabel("Y coordinates of agents")
if title:
plt.title(title)
if save:
plt.savefig(str(time.time()).replace(".", "") + ".jpg")
else:
plt.show()
if __name__ == '__main__':
topology = util.parser(filename=os.path.join(os.getcwd(), "init_files",
"evolutionary_init.txt"))
#topology.plot_topology(order_of_passing=True)