def test_loop(phenotypes, iteration):
"""Test the phenotypes in a completely different environment."""
# Create a validation environment instance
test = TestModel(100, width, height, 10, iter=iteration)
# Build the environment
test.build_environment_from_json()
# Create the agents in the environment from the sampled behaviors
test.create_agents(phenotypes=phenotypes)
# Store the initial result
testresults = SimulationResults(test.pname, test.connect,
test.sn, test.stepcnt,
test.foraging_percent(), phenotypes[0])
# Save the phenotype to a json file
testresults.save_phenotype()
# Save the data in a result csv file
testresults.save_to_file()
# Save the phenotype of json file
phenotype_to_json(test.pname, test.runid, phenotypes)
# Execute the BT in the environment
for i in range(iteration):
test.step()
testresults = SimulationResults(test.pname, test.connect,
test.sn, test.stepcnt,
test.foraging_percent(), phenotypes[0])
testresults.save_to_file()
# Plot the result in the graph
graph = GraphACC(test.pname, 'simulation.csv')
graph.gen_plot()
def simulate_res1(env, iteration, prob=0.5):
"""Test the performane of evolved behavior."""
phenotypes = env[0]
threshold = 1.0
sim = SimModelRes1(150,
100,
100,
10,
iter=iteration,
xmlstrings=phenotypes,
pname=env[1],
prob=prob)
sim.build_environment_from_json()
# for all agents store the information about hub
for agent in sim.agents:
agent.shared_content['Hub'] = {sim.hub}
# agent.shared_content['Sites'] = {sim.site}
simresults = SimulationResults(sim.pname, sim.connect, sim.sn, sim.stepcnt,
sim.food_in_hub(), phenotypes[0])
simresults.save_phenotype()
simresults.save_to_file()
# Iterate and execute each step in the environment
for i in range(iteration):
# For every iteration we need to store the results
# Save them into db or a file
sim.step()
value = sim.food_in_hub()
foraging_percent = (value * 100.0) / (sim.num_agents * 1.0)
simresults = SimulationResults(sim.pname, sim.connect, sim.sn,
sim.stepcnt, foraging_percent,
phenotypes[0])
simresults.save_to_file()
# print ('food at site', len(sim.food_in_loc(sim.site.location)))
# print ('food at hub', len(sim.food_in_loc(sim.hub.location)))
# print("Total food in the hub", len(food_objects))
# food_objects = sim.food_in_loc(sim.hub.location)
# for food in food_objects:
# print('simulate phenotye:', dir(food))
sucess = False
print('Foraging percent', value)
if foraging_percent >= threshold:
print('Foraging success')
sucess = True
sim.experiment.update_experiment_simulation(value, sucess)
# Plot the fitness in the graph
graph = GraphACC(sim.pname, 'simulation.csv')
graph.gen_plot()
def simulate(agent, iteration):
# Testing the performane of evolved behavior
phenotype = agent.individual[0].phenotype
# iteration = 10000
threshold = 75.0
sim = RunEnvironmentModel(100,
100,
100,
10,
iter=iteration,
xmlstring=phenotype)
sim.build_environment_from_json()
# for all agents store the information about hub
for agent in sim.agents:
agent.shared_content['Hub'] = {sim.hub}
simresults = SimulationResults(sim.pname, sim.connect, sim.sn, sim.stepcnt,
sim.food_in_hub(), phenotype)
simresults.save_phenotype()
simresults.save_to_file()
# Iterate and execute each step in the environment
for i in range(iteration):
# For every iteration we need to store the results
# Save them into db or a file
sim.step()
simresults = SimulationResults(sim.pname,
sim.connect, sim.sn, sim.stepcnt,
sim.food_in_hub(), phenotype)
simresults.save_to_file()
# print("Total food in the hub", len(food_objects))
value = sim.food_in_hub()
foraging_percent = (value * 100.0) / (sim.num_agents * 2.0)
sucess = False
if foraging_percent >= threshold:
print('Foraging success')
sucess = True
# sim.experiment.update_experiment_simulation(value, sucess)
sim.experiment.update_experiment_simulation(value, sucess)
# Plot the fitness in the graph
graph = GraphACC(sim.pname, 'simulation.csv')
graph.gen_plot()
def validation_loop(phenotypes,
iteration,
parentname=None,
ratio=1,
threshold=10.0):
"""Validate the evolved behaviors."""
# Create a validation environment instance
# print('len of phenotype', len(set(phenotypes)))
valid = ValidationModel(100,
width,
height,
10,
iter=iteration,
parent=parentname,
ratio=ratio)
# print('parent:', parentname, ' children:', valid.runid)
# Build the environment
valid.build_environment_from_json()
# Create the agents in the environment from the sampled behaviors
valid.create_agents(phenotypes=phenotypes)
# print('total food units', valid.total_food_units)
# Print the BT
# py_trees.display.print_ascii_tree(valid.agents[1].bt.behaviour_tree.root)
# py_trees.logging.level = py_trees.logging.Level.DEBUG
for i in range(iteration):
valid.step()
# print ([agent.location for agent in valid.agents])
validresults = SimulationResults(valid.pname, valid.connect,
valid.sn, valid.stepcnt,
valid.foraging_percent(),
phenotypes[0])
validresults.save_to_file()
# print('food in the hub', valid.agents[0].get_food_in_hub(False))
# Save the phenotype to json file
phenotype_to_json(valid.pname, valid.runid + '-' + str(i), phenotypes)
# Plot the result in the graph
graph = GraphACC(valid.pname, 'simulation.csv')
graph.gen_plot()
# Return true if the sample behavior achieves a threshold
if valid.foraging_percent() > threshold:
return True
else:
return False
def after_simluation(sim, phenotypes, iteration, threshold):
"""Compute stuffs after defining experiment environment."""
# for all agents store the information about hub
for agent in sim.agents:
agent.shared_content['Hub'] = {sim.hub}
# agent.shared_content['Sites'] = {sim.site}
simresults = SimulationResults(sim.pname, sim.connect, sim.sn, sim.stepcnt,
sim.food_in_hub(), phenotypes[0])
simresults.save_phenotype()
simresults.save_to_file()
# Iterate and execute each step in the environment
for i in range(iteration):
# For every iteration we need to store the results
# Save them into db or a file
sim.step()
simresults = SimulationResults(sim.pname,
sim.connect, sim.sn, sim.stepcnt,
sim.food_in_hub(), phenotypes[0])
simresults.save_to_file()
# print ('food at site', len(sim.food_in_loc(sim.site.location)))
# print ('food at hub', len(sim.food_in_loc(sim.hub.location)))
# print("Total food in the hub", len(food_objects))
food_objects = sim.food_in_loc(sim.hub.location)
for food in food_objects:
print('simulate phenotye:', dir(food))
value = sim.food_in_hub()
foraging_percent = (value * 100.0) / (sim.num_agents * 2.0)
sucess = False
print('Foraging percent', value)
if foraging_percent >= threshold:
print('Foraging success')
sucess = True
sim.experiment.update_experiment_simulation(value, sucess)
# Plot the fitness in the graph
graph = GraphACC(sim.pname, 'simulation.csv')
graph.gen_plot()
def after_simulation(sim, phenotypes, iteration, threshold):
"""Step for all simulation after defining environment."""
# for all agents store the information about hub
for agent in sim.agents:
agent.shared_content['Hub'] = {sim.hub}
agent.shared_content['Obstacles'] = set(sim.obstacles)
simresults = SimulationResults(sim.pname, sim.connect, sim.sn, sim.stepcnt,
len(sim.debris_cleaned()), phenotypes[0])
simresults.save_phenotype()
simresults.save_to_file()
# Iterate and execute each step in the environment
for i in range(iteration):
# For every iteration we need to store the results
# Save them into db or a file
sim.step()
debris_objects = sim.debris_cleaned()
value = len(debris_objects)
cleaned_percent = (value * 100.0) / (200.0)
simresults = SimulationResults(sim.pname,
sim.connect, sim.sn, sim.stepcnt,
int(cleaned_percent), phenotypes[0])
simresults.save_to_file()
sucess = False
print('Cleaning percent', value)
if cleaned_percent >= threshold:
print('Debris clean success')
sucess = True
sim.experiment.update_experiment_simulation(value, sucess)
# Plot the fitness in the graph
graph = GraphACC(sim.pname, 'simulation.csv')
graph.gen_plot()
def simulate(env, iteration):
"""Test the performane of evolved behavior."""
# phenotype = agent.individual[0].phenotype
# phenotypes = extract_phenotype(agents)
phenotypes = env[0]
# phenotypes = ['<?xml version=\"1.0\" encoding=\"UTF-8\"?><Sequence><Sequence><Sequence><Selector><cond>IsDropable_Sites</cond><act>Explore</act></Selector><Sequence><cond>NeighbourObjects</cond><act>CompositeSingleCarry_Food</act></Sequence></Sequence> <Sequence><Sequence><cond>NeighbourObjects</cond><act>Explore</act></Sequence> <Sequence><cond>NeighbourObjects</cond><act>MoveTowards_Hub</act></Sequence></Sequence></Sequence> <Sequence><Selector><cond>NeighbourObjects</cond><act>MoveAway_Sites</act></Selector><Sequence><cond>NeighbourObjects</cond><act>CompositeDrop_Food</act></Sequence></Sequence></Sequence>', '<?xml version=\"1.0\" encoding=\"UTF-8\"?><Sequence><Sequence><Sequence><Selector><cond>IsDropable_Sites</cond><act>Explore</act></Selector><Sequence><cond>NeighbourObjects</cond><act>CompositeSingleCarry_Food</act></Sequence></Sequence> <Sequence><Sequence><cond>NeighbourObjects</cond><act>Explore</act></Sequence> <Sequence><cond>NeighbourObjects</cond><act>MoveTowards_Hub</act></Sequence></Sequence></Sequence> <Sequence><Selector><cond>NeighbourObjects</cond><act>MoveAway_Sites</act></Selector><Sequence><cond>NeighbourObjects</cond><act>CompositeDrop_Food</act></Sequence></Sequence></Sequence>','<?xml version=\"1.0\" encoding=\"UTF-8\"?><Sequence><Sequence><Sequence><Selector><cond>IsDropable_Sites</cond><act>Explore</act></Selector><Sequence><cond>NeighbourObjects</cond><act>CompositeSingleCarry_Food</act></Sequence></Sequence> <Sequence><Sequence><cond>NeighbourObjects</cond><act>Explore</act></Sequence> <Sequence><cond>NeighbourObjects</cond><act>MoveTowards_Hub</act></Sequence></Sequence></Sequence> <Sequence><Selector><cond>NeighbourObjects</cond><act>MoveAway_Sites</act></Selector><Sequence><cond>NeighbourObjects</cond><act>CompositeDrop_Food</act></Sequence></Sequence></Sequence>','<?xml version=\"1.0\" encoding=\"UTF-8\"?><Sequence><Sequence><Sequence><Selector><cond>IsDropable_Sites</cond><act>Explore</act></Selector><Sequence><cond>NeighbourObjects</cond><act>CompositeSingleCarry_Food</act></Sequence></Sequence> <Sequence><Sequence><cond>NeighbourObjects</cond><act>Explore</act></Sequence> <Sequence><cond>NeighbourObjects</cond><act>MoveTowards_Hub</act></Sequence></Sequence></Sequence> <Sequence><Selector><cond>NeighbourObjects</cond><act>MoveAway_Sites</act></Selector><Sequence><cond>NeighbourObjects</cond><act>CompositeDrop_Food</act></Sequence></Sequence></Sequence>','<?xml version=\"1.0\" encoding=\"UTF-8\"?><Sequence><Sequence><Sequence><Sequence><Sequence><cond>NeighbourObjects</cond><act>Explore</act></Sequence> <Sequence><cond>NeighbourObjects</cond><act>CompositeSingleCarry_Food</act></Sequence></Sequence> <Sequence><cond>IsDropable_Hub</cond><act>CompositeDrop_Food</act></Sequence></Sequence> <Sequence><cond>IsDropable_Hub</cond><cond>IsDropable_Hub</cond><act>CompositeDrop_Food</act></Sequence></Sequence> <Selector><Selector><cond>IsDropable_Hub</cond><act>MoveTowards_Hub</act></Selector> <Selector><cond>IsDropable_Hub</cond><cond>NeighbourObjects</cond><act>MoveTowards_Sites</act></Selector></Selector></Sequence>','<?xml version=\"1.0\" encoding=\"UTF-8\"?><Sequence><Sequence><Sequence><Sequence><Sequence><cond>NeighbourObjects</cond><act>Explore</act></Sequence> <Sequence><cond>NeighbourObjects</cond><act>CompositeSingleCarry_Food</act></Sequence></Sequence> <Sequence><cond>IsDropable_Hub</cond><act>CompositeDrop_Food</act></Sequence></Sequence> <Sequence><cond>IsDropable_Hub</cond><cond>IsDropable_Hub</cond><act>CompositeDrop_Food</act></Sequence></Sequence> <Selector><Selector><cond>IsDropable_Hub</cond><act>MoveTowards_Hub</act></Selector> <Selector><cond>IsDropable_Hub</cond><cond>NeighbourObjects</cond><act>MoveTowards_Sites</act></Selector></Selector></Sequence>','<?xml version=\"1.0\" encoding=\"UTF-8\"?><Selector><Selector><Sequence><Sequence><cond>IsDropable_Hub</cond><act>Explore</act></Sequence> <Sequence><cond>NeighbourObjects</cond><act>CompositeDrop_Food</act></Sequence></Sequence><Sequence><Sequence><cond>IsDropable_Hub</cond><act>CompositeSingleCarry_Food</act></Sequence> <Selector><cond>NeighbourObjects</cond><act>MoveTowards_Sites</act></Selector></Sequence></Selector> <Selector><Selector><cond>IsDropable_Sites</cond><cond>IsDropable_Hub</cond><act>CompositeSingleCarry_Food</act></Selector> <Selector><cond>IsDropable_Hub</cond><cond>NeighbourObjects</cond><act>CompositeDrop_Food</act></Selector></Selector></Selector>','<?xml version=\"1.0\" encoding=\"UTF-8\"?><Selector><Selector><Sequence><Sequence><cond>IsDropable_Hub</cond><act>Explore</act></Sequence> <Sequence><cond>NeighbourObjects</cond><act>CompositeDrop_Food</act></Sequence></Sequence><Sequence><Sequence><cond>IsDropable_Hub</cond><act>CompositeSingleCarry_Food</act></Sequence> <Selector><cond>NeighbourObjects</cond><act>MoveTowards_Sites</act></Selector></Sequence></Selector> <Selector><Selector><cond>IsDropable_Sites</cond><cond>IsDropable_Hub</cond><act>CompositeSingleCarry_Food</act></Selector> <Selector><cond>IsDropable_Hub</cond><cond>NeighbourObjects</cond><act>CompositeDrop_Food</act></Selector></Selector></Selector>','<?xml version=\"1.0\" encoding=\"UTF-8\"?><Selector><Selector><Sequence><Sequence><cond>IsDropable_Hub</cond><act>Explore</act></Sequence> <Sequence><cond>NeighbourObjects</cond><act>CompositeDrop_Food</act></Sequence></Sequence><Sequence><Sequence><cond>IsDropable_Hub</cond><act>CompositeSingleCarry_Food</act></Sequence> <Selector><cond>NeighbourObjects</cond><act>MoveTowards_Sites</act></Selector></Sequence></Selector> <Selector><Selector><cond>IsDropable_Sites</cond><cond>IsDropable_Hub</cond><act>CompositeSingleCarry_Food</act></Selector> <Selector><cond>IsDropable_Hub</cond><cond>NeighbourObjects</cond><act>CompositeDrop_Food</act></Selector></Selector></Selector>','<?xml version=\"1.0\" encoding=\"UTF-8\"?><Selector><Selector><Sequence><Sequence><cond>IsDropable_Hub</cond><act>Explore</act></Sequence> <Sequence><cond>NeighbourObjects</cond><act>CompositeDrop_Food</act></Sequence></Sequence><Sequence><Sequence><cond>IsDropable_Hub</cond><act>CompositeSingleCarry_Food</act></Sequence> <Selector><cond>NeighbourObjects</cond><act>MoveTowards_Sites</act></Selector></Sequence></Selector> <Selector><Selector><cond>IsDropable_Sites</cond><cond>IsDropable_Hub</cond><act>CompositeSingleCarry_Food</act></Selector> <Selector><cond>IsDropable_Hub</cond><cond>NeighbourObjects</cond><act>CompositeDrop_Food</act></Selector></Selector></Selector>']
threshold = 1.0
sim = SimModelComm(100,
100,
100,
10,
iter=iteration,
xmlstrings=phenotypes,
pname=env[1])
sim.build_environment_from_json()
# for all agents store the information about hub
for agent in sim.agents:
agent.shared_content['Hub'] = {sim.hub}
# agent.shared_content['Sites'] = {sim.site}
simresults = SimulationResults(sim.pname, sim.connect, sim.sn, sim.stepcnt,
sim.food_in_hub(), phenotypes[0])
simresults.save_phenotype()
simresults.save_to_file()
# Iterate and execute each step in the environment
for i in range(iteration):
# For every iteration we need to store the results
# Save them into db or a file
sim.step()
simresults = SimulationResults(sim.pname,
sim.connect, sim.sn, sim.stepcnt,
sim.food_in_hub(), phenotypes[0])
simresults.save_to_file()
# print ('food at site', len(sim.food_in_loc(sim.site.location)))
# print ('food at hub', len(sim.food_in_loc(sim.hub.location)))
# print("Total food in the hub", len(food_objects))
food_objects = sim.food_in_loc(sim.hub.location)
for food in food_objects:
print('simulate phenotye:', dir(food))
value = sim.food_in_hub()
foraging_percent = (value * 100.0) / (sim.num_agents * 2.0)
sucess = False
print('Foraging percent', value)
if foraging_percent >= threshold:
print('Foraging success')
sucess = True
sim.experiment.update_experiment_simulation(value, sucess)
# Plot the fitness in the graph
graph = GraphACC(sim.pname, 'simulation.csv')
graph.gen_plot()