def main():
iteration = 100
env = EnvironmentModel(100, 100, 100, 10, iter=iteration)
env.build_environment_from_json()
# for all agents store the information about hub
for agent in env.agents:
agent.shared_content['Hub'] = {env.hub}
# Hub and site object
print(env.hub, env.site)
# Iterate and execute each step in the environment
for i in range(iteration):
env.step()
env.experiment.update_experiment()
# Find if food has been deposited in the hub
grid = env.grid
food_loc = (0, 0)
neighbours = grid.get_neighborhood(food_loc, 5)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
for food in food_objects:
print('food', food.id, food.location)
# Plot the fitness in the graph
graph = Graph(env.pname, 'best.csv', ['diversity', 'explore'])
graph.gen_best_plots()
"""
def learning_phase(iteration, early_stop=False):
"""Learning Algorithm block."""
# Evolution environment
env = EvolveModel(100, width, height, 10, iter=iteration)
env.build_environment_from_json()
env.create_agents()
# Validation Step parameter
# Run the validation test every these many steps
validation_step = 6000
# Iterate and execute each step in the environment
# Take a step i number of step in evolution environment
# Take a 1000 step in validation environment sampling from the evolution
# Make the validation envronmnet same as the evolution environment
for i in range(iteration):
# Take a step in evolution
env.step()
if (i + 1) % validation_step == 0:
try:
phenotypes = env.behavior_sampling_objects(ratio_value=0.1)
# save the phenotype to json file
phenotype_to_json(
env.pname, env.runid, phenotypes)
validation_loop(
phenotypes, 5000, parentname=env.pname)
except ValueError:
pass
# Plot the fitness in the graph
graph = Graph(
env.pname, 'best.csv', [
'explore', 'foraging', 'prospective', 'fitness'],
pname='best' + str(i))
graph.gen_best_plots()
"""
if early_stop:
# Update the experiment table
env.experiment.update_experiment()
# Return phenotypes
return phenotypes
"""
# Update the experiment table
env.experiment.update_experiment()
allphenotypes = env.behavior_sampling_objects(ratio_value=0.99)
# save the phenotype to json file
phenotype_to_json(
env.pname, env.runid + '-' + 'all', allphenotypes)
try:
# return list(phenotypes.keys())
return phenotypes
except UnboundLocalError:
return None
def evolve(iteration):
"""Learning Algorithm block."""
# iteration = 10000
env = EvolModel(100,
100,
100,
10,
iter=iteration,
expname='COTCommEvolve',
agent='EvolAgent',
parm='swarm_mcarry_comm.txt')
env.build_environment_from_json()
# for all agents store the information about hub
for agent in env.agents:
agent.shared_content['Hub'] = {env.hub}
# Hub and site object
# print(env.hub, env.site)
# Iterate and execute each step in the environment
for i in range(iteration):
env.step()
env.experiment.update_experiment()
# Collecting phenotypes on the basis of food collected
# Find if food has been deposited in the hub
food_objects = env.food_in_loc(env.hub.location)
# print('Total food in the hub evolution:', len(food_objects))
env.phenotypes = []
for food in food_objects:
print(food.phenotype)
env.phenotypes += list(food.phenotype.values())
jfilename = env.pname + '/' + env.runid + '.json'
JsonPhenotypeData.to_json(env.phenotypes, jfilename)
# Not using this method right now
# env.phenotypes = extract_phenotype(env.agents, jfilename)
# Plot the fitness in the graph
graph = Graph(env.pname, 'best.csv', ['explore', 'foraging'])
graph.gen_best_plots()
# Test the evolved behavior
return env
def evolve(iteration):
"""Learning Algorithm block."""
# iteration = 10000
env = EvolModel(100,
100,
100,
10,
iter=iteration,
expname='NMComm',
agent='EvolAgent',
parm='swarm_comm.txt')
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
for i in range(iteration):
env.step()
env.experiment.update_experiment()
# Collecting phenotypes on the basis of debris collected
# Find if debris has been cleaned from the hub
debris_objects = env.debris_cleaned()
env.phenotypes = []
for debris in debris_objects:
print(debris.phenotype)
env.phenotypes += list(debris.phenotype.values())
jfilename = env.pname + '/' + env.runid + '.json'
JsonPhenotypeData.to_json(env.phenotypes, jfilename)
# Not using this method right now
# env.phenotypes = extract_phenotype(env.agents, jfilename)
# Plot the fitness in the graph
graph = Graph(env.pname, 'best.csv', ['explore', 'foraging'])
graph.gen_best_plots()
# Test the evolved behavior
return env
def learning_phase(iteration, early_stop=False):
"""Learning Algorithm block."""
# Evolution environment
env = EvolveModel(100, 100, 100, 10, iter=iteration)
env.build_environment_from_json()
env.create_agents()
# Validation Step parameter
# Run the validation test every these many steps
validation_step = 1000
# Iterate and execute each step in the environment
# Take a step i number of step in evolution environment
# Take a 1000 step in validation environment sampling from the evolution
# Make the validation envronmnet same as the evolution environment
for i in range(iteration):
# Take a step in evolution
env.step()
if (i + 1) % validation_step == 0:
phenotypes = env.behavior_sampling()
# save the phenotype to json file
phenotype_to_json(env.pname, env.runid + '-' + str(i), phenotypes)
# early_stop = validation_loop(phenotypes, 2000)
# Plot the fitness in the graph
graph = Graph(env.pname,
'best.csv',
['explore', 'foraging', 'prospective', 'fitness'],
pname='best' + str(i))
graph.gen_best_plots()
"""
if early_stop:
# Update the experiment table
env.experiment.update_experiment()
# Return phenotypes
return phenotypes
"""
# Update the experiment table
env.experiment.update_experiment()
return phenotypes
def learning_phase(iteration, early_stop=False):
"""Learning Algorithm block."""
# Evolution environment
env = EvolveModel(50, width, height, 10, iter=iteration)
env.build_environment_from_json()
env.create_agents()
# Validation Step parameter
# Run the validation test every these many steps
validation_step = 6000
# Iterate and execute each step in the environment
# Take a step i number of step in evolution environment
# Take a 1000 step in validation environment sampling from the evolution
# Make the validation envronmnet same as the evolution environment
for i in range(iteration):
# Take a step in evolution
env.step()
if (i + 1) % validation_step == 0:
try:
# print([agent.individual[0].fitness for agent in env.agents])
# msg = []
# for agent in env.agents:
# encode = agent.individual[0].phenotype.encode('utf-8')
# msg += [(
# agent.name, hashlib.sha224(encode).hexdigest(
# ), agent.individual[0].fitness)]
# n,p,f = zip(*msg)
# print (i, p[:10])
# ratio = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99]
# for r in ratio:
phenotypes = env.behavior_sampling_objects(ratio_value=0.1)
# save the phenotype to json file
phenotype_to_json(env.pname, env.runid, phenotypes)
# early_stop = validation_loop(phenotypes, 5000)
validation_loop(phenotypes, 5000, parentname=env.pname)
except ValueError:
pass
# Plot the fitness in the graph
graph = Graph(env.pname,
'best.csv',
['explore', 'foraging', 'prospective', 'fitness'],
pname='best' + str(i))
graph.gen_best_plots()
"""
if early_stop:
# Update the experiment table
env.experiment.update_experiment()
# Return phenotypes
return phenotypes
"""
# Update the experiment table
env.experiment.update_experiment()
"""
hashlist = dict()
phenotypes = dict()
# generations = [agent.individual for agent in env.agents]
for agent in env.agents:
encode = agent.individual[0].phenotype.encode('utf-8')
hashval = hashlib.sha224(encode).hexdigest()
print(
agent.name, hashval,
agent.individual[0].fitness, agent.food_collected)
phenotypes[agent.individual[0].phenotype] = agent.individual[0].fitness
try:
hashlist[hashval] += 1
except KeyError:
hashlist[hashval] = 1
print(hashlist)
"""
# print('max, min generations', np.max(generations), np.min(generations))
# pdb.set_trace()
allphenotypes = env.behavior_sampling_objects(ratio_value=0.99)
# save the phenotype to json file
phenotype_to_json(env.pname, env.runid + '-' + 'all', allphenotypes)
try:
# return list(phenotypes.keys())
return phenotypes
except UnboundLocalError:
return None