def extract_phenotype(agents, filename, method='ratio'):
"""Extract phenotype of the learning agents.
Sort the agents based on the overall fitness and then based on the
method extract phenotype of the agents.
Method can take {'ratio','higest','sample'}
"""
sorted_agents = sorted(agents,
key=lambda x: x.individual[0].fitness,
reverse=True)
if method == 'ratio':
ratio_value = 0.4
upper_bound = ratio_value * len(agents)
selected_agents = agents[0:int(upper_bound)]
selected_phenotype = [
agent.individual[0].phenotype for agent in selected_agents
]
# return selected_phenotype
else:
selected_phenotype = [sorted_agents[0].individual[0].phenotype]
# return [sorted_agents[0].individual[0].phenotype]
# Save the phenotype to a json file
JsonPhenotypeData.to_json(selected_phenotype, filename)
# Return the phenotype
return selected_phenotype
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 phenotype_to_json(pname, runid, phenotypes):
"""Store the phenotype to a json file."""
jfilename = pname + '/' + runid + '.json'
JsonPhenotypeData.to_json(phenotypes, jfilename)