self.genotype.orig_size_xyz) * min_percent_full:
return False
# Discarding the robot if it doesn't have at least a given percentage of muscles (materials 3 and 4)
if count_occurrences(state, [3, 4]) < np.product(
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)
if np.sum(state > 0) < np.product(
self.genotype.orig_size_xyz) * min_percent_full:
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,
self.genotype.orig_size_xyz) * min_percent_full:
return False
# Discarding the robot if it doesn't have at least a given percentage of muscles (materials 3 and 4)
if count_occurrences(state, [3, 4]) < np.product(
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)
# 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>")
symmetric=False))
self.to_phenotype_mapping.add_map(name="init_offset",
tag="<PhaseOffset>")
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,
self.to_phenotype_mapping.add_map(name="init_size",
tag="<InitialVoxelSize>")
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,
growth_amp=GROWTH_AMPLITUDE)
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,
tag="<PhaseOffset>")
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,
scale=MUT_SCALE,
symmetric=False))
self.to_phenotype_mapping.add_map(name="init_offset",
tag="<PhaseOffset>")
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="<FinalVoxelSize>")
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,
min_temp_fact=MIN_TEMP_FACT,
fitness_eval_init_time=INIT_TIME,
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>")
self.add_network(DirectEncoding(output_node_name="init_offset", orig_size_xyz=IND_SIZE,
scale=MUT_SCALE, symmetric=False))
self.to_phenotype_mapping.add_map(name="init_offset", tag="<PhaseOffset>")
self.add_network(DirectEncoding(output_node_name="final_offset", orig_size_xyz=IND_SIZE,
scale=MUT_SCALE, symmetric=False))
self.to_phenotype_mapping.add_map(name="final_offset", tag="<FinalPhaseOffset>")
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)
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="lifetime", maximize=True, tag="<Lifetime>", logging_only=True)
my_objective_dict.add_objective(name="age", maximize=False, tag=None)
my_pop = Population(my_objective_dict, MyGenotype, Phenotype, pop_size=POP_SIZE)
names = [k for k, v in NET_DICT.items()]
nets = [v for k, v in NET_DICT.items()]
my_pop.replace_ind_networks(names, nets)
my_optimization = SetMutRateOptimization(my_sim, my_env, my_pop, MUT_NET_PROB_DIST)
my_optimization.run(max_hours_runtime=MAX_TIME, max_gens=MAX_GENS, num_random_individuals=NUM_RANDOM_INDS,
state,
[3, 4]) < np.sum(state > 0) * 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(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",
return False
# Discarding the robot if it doesn't have at least a given percentage of muscles (materials 3 and 4)
if count_occurrences(state, [3, 4]) < np.product(
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=0.5,
save_traces=1,
novelty_based=True)
# 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>")
my_objective_dict.add_objective(name="energy",
maximize=False,
tag=None,
