def simulate_res1(env, iteration):
"""Test the performane of evolved behavior."""
phenotypes = env[0]
threshold = 1.0
sim = SimModel(
100, 100, 100, 10, iter=iteration, xmlstrings=phenotypes, pname=env[1],
expname='MSFRes1', agent='SimAgentRes1')
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()
def main():
iteration = 1500
env = SimModel(100, width, height, 10, iter=iteration, viewer=False)
env.build_environment_from_json()
# for all agents store the information about hub
for agent in env.agents:
agent.shared_content['Hub'] = {env.hub}
# Iterate and execute each step in the environment
# print ('Step', 'Name', 'TS', 'DEL', 'OVF', 'EXP', 'CAR', 'FOR', 'GNM')
for i in range(iteration):
env.step()
print(len(env.food_in_loc(env.hub.location)))
def simulate_res1(env, iteration):
"""Test the performane of evolved behavior with type 1 resilience."""
phenotypes = env[0]
threshold = 1.0
sim = SimModel(100,
100,
100,
10,
iter=iteration,
xmlstrings=phenotypes,
pname=env[1],
expname='NMRes1',
agent='SimAgentRes1')
sim.build_environment_from_json()
after_simulation(sim, phenotypes, iteration, threshold)
def simulate_res2(env, iteration):
"""Test the performane of evolved behavior."""
phenotypes = env[0]
threshold = 1.0
sim = SimModel(100,
100,
100,
10,
iter=iteration,
xmlstrings=phenotypes,
pname=env[1],
expname='COTCommRes2',
agent='SimAgentResComm2')
sim.build_environment_from_json()
after_simluation(sim, phenotypes, iteration, threshold)
def simulate(env, iteration):
"""Test the performane of evolved behavior."""
# phenotype = agent.individual[0].phenotype
# phenotypes = extract_phenotype(agents)
phenotypes = env[0]
threshold = 1.0
sim = SimModel(100,
100,
100,
10,
iter=iteration,
xmlstrings=phenotypes,
pname=env[1])
sim.build_environment_from_json()
after_simulation(sim, phenotypes, iteration, threshold)
def simulate(env, iteration, N=100):
"""Test the performane of evolved behavior."""
phenotypes = env[0]
threshold = 1.0
sim = SimModel(N,
100,
100,
10,
seed=None,
iter=iteration,
xmlstrings=phenotypes,
pname=env[1])
sim.build_environment_from_json()
# print(len(sim.obstacles), len(sim.debris))
# py_trees.display.print_ascii_tree(sim.agents[0].bt.behaviour_tree.root)
after_simulation(sim, phenotypes, iteration, threshold)