return False
return True
if not os.path.isfile("./" + RUN_DIR + "/pickledPops/Gen_0.pickle"):
random.seed(SEED)
np.random.seed(SEED)
my_sim = Sim(dt_frac=DT_FRAC,
simulation_time=SIM_TIME,
fitness_eval_init_time=INIT_TIME)
my_env = Env(temp_amp=TEMP_AMP, frequency=FREQ, density=DENSITY)
my_objective_dict = ObjectiveDict()
my_objective_dict.add_objective(name="fitness",
maximize=True,
tag="<normAbsoluteDisplacement>")
if AGE_PROTECTION:
my_objective_dict.add_objective(name="age", maximize=False, tag=None)
my_pop = Population(my_objective_dict,
MyGenotype,
MyPhenotype,
pop_size=POP_SIZE)
my_optimization = ParetoOptimization(my_sim, my_env, my_pop)
my_optimization.run(max_hours_runtime=MAX_TIME,
max_gens=MAX_GENS,
num_random_individuals=NUM_RANDOM_INDS,
if not os.path.isfile("./" + RUN_DIR + "/checkpoint.pickle"):
random.seed(SEED)
np.random.seed(SEED)
my_sim = Sim(dt_frac=DT_FRAC,
simulation_time=SIM_TIME,
min_temp_fact=MIN_TEMP_FACT,
fitness_eval_init_time=INIT_TIME)
my_env = Env(temp_amp=TEMP_AMP)
my_env.add_param("growth_amplitude", GROWTH_AMPLITUDE, "<GrowthAmplitude>")
my_objective_dict = ObjectiveDict()
my_objective_dict.add_objective(name="fitness",
maximize=True,
tag="<finalDistY>")
my_objective_dict.add_objective(name="age", maximize=False, tag=None)
my_pop = Population(my_objective_dict,
MyGenotype,
Phenotype,
pop_size=POP_SIZE)
my_optimization = ParetoOptimization(my_sim, my_env, my_pop)
my_optimization.run(max_hours_runtime=MAX_TIME,
max_gens=MAX_GENS,
num_random_individuals=NUM_RANDOM_INDS,
directory=RUN_DIR,
self.genotype.orig_size_xyz) * min_percent_muscle:
return False
return True
# Setting up the simulation object
my_sim = Sim(dt_frac=DT_FRAC,
simulation_time=SIM_TIME,
fitness_eval_init_time=INIT_TIME)
# Setting up the environment object
my_env = Env(sticky_floor=0, time_between_traces=0)
# Now specifying the objectives for the optimization.
# Creating an objectives dictionary
my_objective_dict = ObjectiveDict()
# Adding an objective named "fitness", which we want to maximize. This information is returned by Voxelyze
# in a fitness .xml file, with a tag named "NormFinalDist"
my_objective_dict.add_objective(name="PushDist",
maximize=False,
tag="<PushDist>")
my_objective_dict.add_objective(name="fitness", maximize=True, tag="<PushRot>")
my_objective_dict.add_objective(name="RotVel",
maximize=True,
tag="<RotVel>",
logging_only=True)
#my_objective_dict.add_objective(name="fitness", maximize=True, tag="<NormFinalDist>")
# Add an objective to minimize the age of solutions: promotes diversity
my_objective_dict.add_objective(name="age", maximize=False, tag=None)
MyGenotype.NET_DICT = {"material": post_damage_shape}
if not os.path.isfile("./" + RUN_DIR + "/pickledPops/Gen_0.pickle"):
random.seed(SEED)
np.random.seed(SEED)
my_sim = Sim(dt_frac=DT_FRAC,
simulation_time=SIM_TIME,
fitness_eval_init_time=INIT_TIME,
min_temp_fact=MIN_TEMP_FACT)
my_env = Env(temp_amp=TEMP_AMP, frequency=FREQ, muscle_stiffness=STIFFNESS)
my_objective_dict = ObjectiveDict()
my_objective_dict.add_objective(name="fitness",
maximize=True,
tag=FITNESS_TAG)
my_objective_dict.add_objective(name="age", maximize=False, tag=None)
my_pop = Population(my_objective_dict,
MyGenotype,
Phenotype,
pop_size=POP_SIZE)
my_optimization = ParetoOptimization(my_sim, my_env, my_pop)
my_optimization.run(max_hours_runtime=MAX_TIME,
max_gens=MAX_GENS,
num_random_individuals=NUM_RANDOM_INDS,
directory=RUN_DIR,
# Setting up the simulation object
my_sim = Sim(dt_frac=DT_FRAC,
simulation_time=SIM_TIME,
fitness_eval_init_time=INIT_TIME)
# Setting up the environment object
my_env = Env(sticky_floor=0, time_between_traces=0)
# Here we tell the physics engine that we want to simulate a fluid environment
my_env.add_param("fluid_environment", 1, "<FluidEnvironment>")
my_env.add_param("aggregate_drag_coefficient", AGGREGATE_DRAG_COEF,
"<AggregateDragCoefficient>")
# Now specifying the objectives for the optimization.
# Creating an objectives dictionary
my_objective_dict = ObjectiveDict()
# Adding an objective named "fitness", which we want to maximize. This information is returned by Voxelyze
# in a fitness .xml file, with a tag named "NormFinalDist"
my_objective_dict.add_objective(name="fitness",
maximize=True,
tag="<normAbsoluteDisplacement>")
# Add an objective to minimize the age of solutions: promotes diversity
my_objective_dict.add_objective(name="age", maximize=False, tag=None)
# Adding another objective called "num_voxels", which we want to minimize in order to minimize
# the amount of material employed to build the robot, promoting at the same time non-trivial
# morphologies.
# This information can be computed in Python (it's not returned by Voxelyze, thus tag=None),
# which is done by counting the non empty voxels (material != 0) composing the robot.
random.seed(SEED)
np.random.seed(SEED)
my_sim = Sim(dt_frac=DT_FRAC,
simulation_time=SIM_TIME,
min_temp_fact=MIN_TEMP_FACT,
fitness_eval_init_time=INIT_TIME)
my_env = Env(temp_amp=TEMP_AMP,
falling_prohibited=STOP_AFTER_FALLING,
time_between_traces=TIME_BETWEEN_TRACES)
my_env.add_param("growth_amplitude", GROWTH_AMPLITUDE, "<GrowthAmplitude>")
my_env.add_param("save_traces", int(SAVE_TRACES), "<SaveTraces>")
my_objective_dict = ObjectiveDict()
my_objective_dict.add_objective(name="fitness",
maximize=True,
tag="<finalDistY>")
my_objective_dict.add_objective(name="age", maximize=False, tag=None)
my_objective_dict.add_objective(name="lifetime",
maximize=True,
tag="<Lifetime>",
logging_only=True)
my_objective_dict.add_objective(name="trace",
maximize=True,
tag="<CMTrace>",
logging_only=True)
my_pop = Population(my_objective_dict,
MyGenotype,
self.genotype.orig_size_xyz) * min_percent_muscle:
return False
return True
# Setting up the simulation object
my_sim = Sim(dt_frac=DT_FRAC,
simulation_time=SIM_TIME,
fitness_eval_init_time=INIT_TIME)
# Setting up the environment object
my_env = Env(sticky_floor=0, time_between_traces=0)
# Now specifying the objectives for the optimization.
# Creating an objectives dictionary
my_objective_dict = ObjectiveDict()
# Adding an objective named "fitness", which we want to maximize. This information is returned by Voxelyze
# in a fitness .xml file, with a tag named "NormFinalDist"
my_objective_dict.add_objective(name="fitness",
maximize=True,
tag="<NormFinalDist>")
# Initializing a population of SoftBots
my_pop = Population(my_objective_dict,
MyGenotype,
MyPhenotype,
pop_size=POPSIZE)
# Setting up our optimization
my_optimization = ParetoOptimization(my_sim, my_env, my_pop)
self.genotype.orig_size_xyz) * min_percent_muscle:
return False
return True
# Setting up the simulation object
my_sim = Sim(dt_frac=DT_FRAC,
simulation_time=SIM_TIME,
fitness_eval_init_time=INIT_TIME)
# Setting up the environment object
my_env = Env(sticky_floor=0, time_between_traces=0)
# Now specifying the objectives for the optimization.
# Creating an objectives dictionary
my_objective_dict = ObjectiveDict()
# Adding an objective named "fitness", which we want to maximize. This information is returned by Voxelyze
# in a fitness .xml file, with a tag named "NormFinalDist"
#my_objective_dict.add_objective(name="PushDist", maximize=False, tag="<PushDist>")
#my_objective_dict.add_objective(name="PushRot", maximize=True, tag="<PushRot>")
my_objective_dict.add_objective(name="fitness",
maximize=True,
tag="<PushRotUnwr>")
#my_objective_dict.add_objective(name="RotVel", maximize=True, tag="<RotVel>", logging_only=True)
#my_objective_dict.add_objective(name="fitness", maximize=True, tag="<NormFinalDist>")
# Add an objective to minimize the age of solutions: promotes diversity
my_objective_dict.add_objective(name="age", maximize=False, tag=None)
# Adding another objective called "num_voxels", which we want to minimize in order to minimize
# the amount of material employed to build the robot, promoting at the same time non-trivial
afterlife_time=AFTERLIFE_TIME,
mid_life_freeze_time=MID_LIFE_FREEZE_TIME)
my_env = Env(temp_amp=TEMP_AMP, time_between_traces=TIME_BETWEEN_TRACES)
my_env.add_param("growth_amplitude", GROWTH_AMPLITUDE, "<GrowthAmplitude>")
my_env.add_param("min_growth_time", MIN_GROWTH_TIME, "<MinGrowthTime>")
my_env.add_param("falling_prohibited", int(FALLING_PROHIBITED),
"<FallingProhibited>")
my_env.add_param("norm_dist_by_vol", int(NORMALIZE_DIST_BY_VOL),
"<NormDistByVol>")
my_env.add_param("normalization_exponent", int(NORMALIZATION_EXPONENT),
"<NormalizationExponent>")
my_env.add_param("save_traces", int(SAVE_TRACES), "<SaveTraces>")
my_objective_dict = ObjectiveDict()
my_objective_dict.add_objective(name="fitness",
maximize=True,
tag="<NormFinalDist>")
my_objective_dict.add_objective(name="age", maximize=False, tag=None)
my_objective_dict.add_objective(name="frozen_dist",
maximize=True,
tag="<NormFrozenDist>",
logging_only=True)
my_pop = Population(my_objective_dict,
MyGenotype,
Phenotype,
pop_size=POP_SIZE)
my_optimization = ParetoOptimization(my_sim, my_env, my_pop)
self.genotype.orig_size_xyz) * min_percent_muscle:
return False
return True
# Setting up the simulation object
my_sim = Sim(dt_frac=DT_FRAC,
simulation_time=SIM_TIME,
fitness_eval_init_time=INIT_TIME)
# Setting up the environment object
my_env = Env(sticky_floor=0, time_between_traces=0)
# Now specifying the objectives for the optimization.
# Creating an objectives dictionary
my_objective_dict = ObjectiveDict()
# Adding an objective named "fitness", which we want to maximize. This information is returned by Voxelyze
# in a fitness .xml file, with a tag named "NormFinalDist"
my_objective_dict.add_objective(name="fitness",
maximize=True,
tag="<NormFinalDist>")
# Adding another objective named "old_materials", which should be minimized.
# This information is computed in Python as the occurrences of old materials (materials number 1, 2, 3 and 4)
my_objective_dict.add_objective(name="old_materials",
maximize=False,
tag=None,
node_func=partial(count_occurrences,
keys=[1, 2, 3, 4]),
output_node_name="material")
self.genotype.orig_size_xyz) * min_percent_muscle:
return False
return True
# Setting up the simulation object
my_sim = Sim(dt_frac=DT_FRAC,
simulation_time=SIM_TIME,
fitness_eval_init_time=INIT_TIME)
# Setting up the environment object
my_env = Env(sticky_floor=0, time_between_traces=0)
# Now specifying the objectives for the optimization.
# Creating an objectives dictionary
my_objective_dict = ObjectiveDict()
# Adding an objective named "fitness", which we want to maximize. This information is returned by Voxelyze
# in a fitness .xml file, with a tag named "NormFinalDist"
my_objective_dict.add_objective(name="fitness",
maximize=True,
tag="<NormFinalDist>")
# Adding another objective named "energy", which should be minimized.
# This information is computed in Python as the occurrences of active materials (materials number 3 and 4)
my_objective_dict.add_objective(name="energy",
maximize=False,
tag=None,
node_func=partial(count_occurrences,
keys=[3, 4]),
output_node_name="material")
# Setting up the simulation object
my_sim = Sim(dt_frac=DT_FRAC,
simulation_time=SIM_TIME,
fitness_eval_init_time=INIT_TIME)
# Setting up the environment object
my_env = Env(obstacles=True,
ind_size=IND_SIZE,
env_size=ENV_SIZE,
init_num_obstacles=INIT_NUM_OBSTACLES,
time_between_traces=0.1)
# Now specifying the objectives for the optimization.
# Creating an objectives dictionary
my_objective_dict = ObjectiveDict()
'''
# Adding an objective named "L-coeff", which we want to maximize. This information is returned by Voxelyze
# in a fitness .xml file, with a tag named "LCoefficient"
my_objective_dict.add_objective(name="fitness", maximize=True, tag="<LCoefficient>")
'''
# Adding an objective named "fitness", which we want to maximize. This information is returned by Voxelyze
# in a fitness .xml file, with a tag named "MaxXYDist"
my_objective_dict.add_objective(name="fitness",
maximize=True,
tag="<MaxXYDist>")
# Adding an objective named "L-coeff", which we want to maximize. This information is returned by Voxelyze
# in a fitness .xml file, with a tag named "LCoefficient"
my_objective_dict.add_objective(name="L-coeff",