def main():
# Get behavior
behavior_function = agent_behavior_base.behavior
# Open log file
logfile = open('average_baseline.txt', 'w')
logfile.write('gen, survive, avgF\n')
print('gen, survive, avgF')
for gen in range(200):
sim_count = 10
# Average over sim_count simulations
fit_sum = 0
survive_sum = 0
for i in range(sim_count):
# Create and initialize environment
env = environment.Environment(behavior_function, dt = .1, sim_time = 0,
time_out = 50, hash_map_grid_size = 40, width = 800, height = 600,
show_bins = False)
env.create_perimeter_walls(location = 'inside', thickness = 5) # Walls
create.create_test_terrain(env) # Terrain
create.create_random_swarm(env, count = 10, radius = 20) # Swarm
sim_time, survivor_count, total_agent_health, predator_health = env.run_sim_no_gfx()
fitness = total_agent_health - predator_health
fit_sum += fitness
survive_sum += survivor_count
log_string = '{:>3d}, {:>4.1f}, {:>7.1f}'.format(gen, survive_sum/sim_count, fit_sum/sim_count)
logfile.write('{:}\n'.format(log_string))
print(log_string)
logfile.close()
def main():
# Get behavior
behavior_function = behavior_from_json('test11_data.json')
#behavior_function = agent_behavior_base.behavior
sim_count = 30
# Average over sim_count simulations
fit_sum = 0
survive_sum = 0
for i in range(sim_count):
# Create and initialize environment
env = environment.Environment(behavior_function, dt = .1, sim_time = 0,
time_out = 50, hash_map_grid_size = 40, width = 800, height = 600,
show_bins = False)
env.create_perimeter_walls(location = 'inside', thickness = 5) # Walls
create.create_test_terrain(env) # Terrain
create.create_random_swarm(env, count = 10, radius = 20) # Swarm
sim_time, survivor_count, total_agent_health, predator_health = env.run_sim_no_gfx()
fitness = total_agent_health - predator_health
fit_sum += fitness
survive_sum += survivor_count
print('trial {:}/{:}, fitness = {:}'.format(i+1, sim_count, fitness), end='\r')
print('\navg fitness over {:} simulations = {:.1f}'.format(sim_count, fit_sum / sim_count))
def main():
# Get behavior
behavior_function = behavior_from_json('test11_data.json')
#behavior_function = agent_behavior_base.behavior
sim_count = 30
# Average over sim_count simulations
fit_sum = 0
survive_sum = 0
for i in range(sim_count):
# Create and initialize environment
env = environment.Environment(behavior_function,
dt=.1,
sim_time=0,
time_out=50,
hash_map_grid_size=40,
width=800,
height=600,
show_bins=False)
env.create_perimeter_walls(location='inside', thickness=5) # Walls
create.create_test_terrain(env) # Terrain
create.create_random_swarm(env, count=10, radius=20) # Swarm
sim_time, survivor_count, total_agent_health, predator_health = env.run_sim_no_gfx(
)
fitness = total_agent_health - predator_health
fit_sum += fitness
survive_sum += survivor_count
print('trial {:}/{:}, fitness = {:}'.format(i + 1, sim_count, fitness),
end='\r')
print('\navg fitness over {:} simulations = {:.1f}'.format(
sim_count, fit_sum / sim_count))
def main():
# Get behavior
behavior_function = agent_behavior_base.behavior
# Open log file
logfile = open('average_baseline.txt', 'w')
logfile.write('gen, survive, avgF\n')
print('gen, survive, avgF')
for gen in range(200):
sim_count = 10
# Average over sim_count simulations
fit_sum = 0
survive_sum = 0
for i in range(sim_count):
# Create and initialize environment
env = environment.Environment(behavior_function,
dt=.1,
sim_time=0,
time_out=50,
hash_map_grid_size=40,
width=800,
height=600,
show_bins=False)
env.create_perimeter_walls(location='inside', thickness=5) # Walls
create.create_test_terrain(env) # Terrain
create.create_random_swarm(env, count=10, radius=20) # Swarm
sim_time, survivor_count, total_agent_health, predator_health = env.run_sim_no_gfx(
)
fitness = total_agent_health - predator_health
fit_sum += fitness
survive_sum += survivor_count
log_string = '{:>3d}, {:>4.1f}, {:>7.1f}'.format(
gen, survive_sum / sim_count, fit_sum / sim_count)
logfile.write('{:}\n'.format(log_string))
print(log_string)
logfile.close()
def simulator(input, output): for index, individual, sim_time_step, sim_time_out, sim_count in iter(input.get, 'STOP'): # Average over sim_count simulations fit_sum = 0 survive_sum = 0 for i in range(sim_count): # Create and initialize environment env = environment.Environment(individual.behavior, dt = sim_time_step, sim_time = 0, time_out = sim_time_out, hash_map_grid_size = 40, width = 800, height = 600, show_bins = False) env.create_perimeter_walls(location = 'inside', thickness = 5) # Walls create.create_test_terrain(env) # Terrain create.create_random_swarm(env, count = 10, radius = 20) # Swarm sim_time, survivor_count, total_agent_health, predator_health = env.run_sim_no_gfx() fitness = total_agent_health - predator_health fit_sum += fitness survive_sum += survivor_count result = (index, fit_sum/sim_count, survive_sum/sim_count) output.put(result)
from simulation_environment import create
from simulation_environment import environment
from simulation_environment import agent_behavior_base
# Baseline
agent_behavior_function = agent_behavior_base.behavior
# Create and initialize environment
environment = environment.Environment(agent_behavior_function, dt = .1, sim_time = 0, time_out = 150,
hash_map_grid_size = 40, width = 800, height = 600, show_bins = False)
environment.create_perimeter_walls(location = 'inside', thickness = 5) # Walls
create.create_test_terrain(environment) # Terrain
create.create_random_swarm(environment, count = 10, radius = 20) # Swarm
time, survivor_count, total_agent_health, predator_health = environment.run_sim_no_gfx()
print("time = ", time)
print("survivor_count = ", survivor_count)
print("total_agent_health = ", total_agent_health)
print("predator_health = ", predator_health)
elite_individual.a_lgp.prog = individual_data['a_lgp']
return elite_individual.behavior
# Genetic program loaded from json file
agent_behavior_function = behavior_from_json('test13_data.json')
#agent_behavior_function = agent_behavior_base.behavior
# Create and initialize environment
env = environment.Environment(agent_behavior_function,
dt=.1,
sim_time=0,
time_out=100,
hash_map_grid_size=40,
width=800,
height=600,
show_bins=False,
run_max_speed=True)
env.create_perimeter_walls(location='inside', thickness=5) # Walls
create.create_test_terrain(env) # Terrain
create.create_random_swarm(env, count=10, radius=20) # Swarm
# Create active action manager
active_actions = active_actions.ActiveActions(env)
pyglet.clock.schedule_interval(update,
1 / 120) # Update display 60 times per second
window.push_handlers(event_handlers.EventHandlers(env, window, active_actions))
pyglet.app.run() # Run pyglet
max_fit = float('-inf')
elite_individual = None
for i, individual_data in enumerate(pop_data):
if individual_data['_fit'] > max_fit:
max_fit = individual_data['_fit']
elite_individual = lgp.Behavior()
elite_individual.s_lgp.prog = individual_data['s_lgp']
elite_individual.a_lgp.prog = individual_data['a_lgp']
return elite_individual.behavior
# Genetic program loaded from json file
agent_behavior_function = behavior_from_json('test13_data.json')
#agent_behavior_function = agent_behavior_base.behavior
# Create and initialize environment
env = environment.Environment(agent_behavior_function, dt = .1, sim_time = 0, time_out = 100,
hash_map_grid_size = 40, width = 800, height = 600, show_bins = False, run_max_speed = True)
env.create_perimeter_walls(location = 'inside', thickness = 5) # Walls
create.create_test_terrain(env) # Terrain
create.create_random_swarm(env, count = 10, radius = 20) # Swarm
# Create active action manager
active_actions = active_actions.ActiveActions(env)
pyglet.clock.schedule_interval(update, 1/120) # Update display 60 times per second
window.push_handlers(event_handlers.EventHandlers(env, window, active_actions))
pyglet.app.run() # Run pyglet
from simulation_environment import create
from simulation_environment import environment
from simulation_environment import agent_behavior_base
# Baseline
agent_behavior_function = agent_behavior_base.behavior
# Create and initialize environment
environment = environment.Environment(
agent_behavior_function,
dt=0.1,
sim_time=0,
time_out=150,
hash_map_grid_size=40,
width=800,
height=600,
show_bins=False,
)
environment.create_perimeter_walls(location="inside", thickness=5) # Walls
create.create_test_terrain(environment) # Terrain
create.create_random_swarm(environment, count=10, radius=20) # Swarm
time, survivor_count, total_agent_health, predator_health = environment.run_sim_no_gfx()
print("time = ", time)
print("survivor_count = ", survivor_count)
print("total_agent_health = ", total_agent_health)
print("predator_health = ", predator_health)
