def test_balance():
""" Test balance scenario """
gp_par = gp.GpParameters()
gp_par.ind_start_length = 8
gp_par.n_population = 16
gp_par.f_crossover = 0.5
gp_par.n_offspring_crossover = 2
gp_par.replace_crossover = False
gp_par.f_mutation = 0.5
gp_par.n_offspring_mutation = 2
gp_par.parent_selection = gp.SelectionMethods.RANK
gp_par.survivor_selection = gp.SelectionMethods.RANK
gp_par.f_elites = 0.1
gp_par.f_parents = gp_par.f_elites
gp_par.mutate_co_offspring = False
gp_par.mutate_co_parents = True
gp_par.mutation_p_add = 0.4
gp_par.mutation_p_delete = 0.3
gp_par.allow_identical = False
gp_par.plot = True
gp_par.n_generations = 300
gp_par.verbose = False
gp_par.fig_last_gen = False
behavior_tree.load_settings_from_file(
'duplo_state_machine/BT_SETTINGS_BALANCE.yaml')
planner_baseline = ['s(', 'f(', '0 at pos (0.0, 0.0, 0.0192)?', \
's(', 'f(', '1 at pos (0.0, 0.0, 0.0)?', 'put 1 at (0.0, 0.0, 0.0)!', ')', \
'f(', '0 on 1?', 'put 0 on 1!', ')', 'apply force 0!', ')', ')', ')']
solved = ['f(', '0 at pos (0.0, 0.0, 0.0192)?', \
's(', 'put 2 at (0.0, 0.0, 0.0)!', 'put 0 on 2!', 'apply force 0!', ')', ')']
start_positions = []
start_positions.append(Pos(-0.05, -0.1, 0.0))
start_positions.append(Pos(0.0, -0.1, 0.0))
start_positions.append(Pos(0.05, -0.1, 0.0))
targets = []
targets.append(Pos(0.0, 0.0, 0.0192))
fitness_coeff = Coefficients()
fitness_coeff.hand_not_empty = 100.0
environment = sm_environment.Environment(start_positions, targets, verbose=False, \
mode=sm.SMMode.BALANCE, fitness_coeff=fitness_coeff)
fitness = environment.get_fitness(planner_baseline)
assert fitness < -10
fitness = environment.get_fitness(solved)
assert fitness > -1
gp_par.log_name = 'test_balance'
_, _, best_fitness, _ = gp.run(environment,
gp_par,
baseline=planner_baseline)
assert best_fitness[-1] > -1
behavior_tree.load_settings_from_file(
'behavior_tree_learning/tests/BT_TEST_SETTINGS.yaml')
def test_croissant():
""" Test croissant scenario """
gp_par = gp.GpParameters()
gp_par.ind_start_length = 8
gp_par.n_population = 16
gp_par.f_crossover = 0.5
gp_par.n_offspring_crossover = 2
gp_par.replace_crossover = False
gp_par.f_mutation = 0.5
gp_par.n_offspring_mutation = 2
gp_par.parent_selection = gp.SelectionMethods.RANK
gp_par.survivor_selection = gp.SelectionMethods.RANK
gp_par.f_elites = 0.1
gp_par.f_parents = gp_par.f_elites
gp_par.mutate_co_offspring = False
gp_par.mutate_co_parents = True
gp_par.mutation_p_add = 0.4
gp_par.mutation_p_delete = 0.3
gp_par.allow_identical = False
gp_par.plot = True
gp_par.n_generations = 1000
gp_par.verbose = False
gp_par.fig_last_gen = False
behavior_tree.load_settings_from_file(
'duplo_state_machine/BT_SETTINGS_CROISSANT.yaml')
planner_baseline = ['s(', 'f(', '0 at pos (0.0, 0.0, 0.0)?', \
's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.0, 0.0)!', ')', ')', \
'f(', '1 at pos (0.0, 0.0, 0.0192)?', \
's(', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', \
'apply force 1!', ')', ')', \
'f(', '2 at pos (0.016, -0.032, 0.0)?', \
's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place at (0.016, -0.032, 0.0)!', ')', ')', \
'f(', '3 at pos (0.016, 0.032, 0.0)?', \
's(', 'f(', 'picked 3?', 'pick 3!', ')', 'place at (0.016, 0.032, 0.0)!', ')', ')', ')']
solved = ['s(', 'f(', '0 at pos (0.0, 0.0, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.0, 0.0)!', ')', ')', \
'f(', '2 at pos (0.016, -0.032, 0.0)?', \
's(', 'pick 2!', 'place at (0.016, -0.032, 0.0)!', ')', ')', \
'f(', '3 at pos (0.016, 0.032, 0.0)?', \
's(', 'pick 3!', 'place at (0.016, 0.032, 0.0)!', ')', ')', \
'f(', '1 at pos (0.0, 0.0, 0.0192)?', \
's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', \
'apply force 1!', ')', ')', ')']
start_positions = []
start_positions.append(Pos(-0.05, -0.1, 0))
start_positions.append(Pos(0, -0.1, 0))
start_positions.append(Pos(0.05, -0.1, 0))
start_positions.append(Pos(0.1, -0.1, 0))
targets = []
targets.append(Pos(0.0, 0.0, 0.0))
targets.append(Pos(0.0, 0.0, 0.0192))
targets.append(Pos(0.016, -0.032, 0.0))
targets.append(Pos(0.016, 0.032, 0.0))
environment = sm_environment.Environment(start_positions,
targets,
verbose=False,
mode=sm.SMMode.CROISSANT)
fitness = environment.get_fitness(planner_baseline)
assert fitness < -100
fitness = environment.get_fitness(solved)
assert fitness > -3
gp_par.log_name = 'test_croissant'
_, _, best_fitness, _ = gp.run(environment,
gp_par,
baseline=planner_baseline)
assert best_fitness[-1] > -4
behavior_tree.load_settings_from_file(
'behavior_tree_learning/tests/BT_TEST_SETTINGS.yaml')
def test_baselining():
# pylint: disable=too-many-statements
""" Tests various baseline setups in the blocking task """
gp_par = gp.GpParameters()
gp_par.ind_start_length = 8
gp_par.n_population = 16
gp_par.f_crossover = 0.5
gp_par.n_offspring_crossover = 2
gp_par.replace_crossover = False
gp_par.f_mutation = 0.5
gp_par.n_offspring_mutation = 2
gp_par.parent_selection = gp.SelectionMethods.RANK
gp_par.survivor_selection = gp.SelectionMethods.RANK
gp_par.f_elites = 0.1
gp_par.f_parents = gp_par.f_elites
gp_par.mutate_co_offspring = False
gp_par.mutate_co_parents = True
gp_par.mutation_p_add = 0.4
gp_par.mutation_p_delete = 0.3
gp_par.allow_identical = False
gp_par.plot = True
gp_par.verbose = False
gp_par.fig_last_gen = False
behavior_tree.load_settings_from_file(
'duplo_state_machine/BT_SETTINGS_BLOCKING.yaml')
planner_baseline = ['s(', 'f(', '0 at pos (-0.1, 0.0, 0.0)?', 'put 0 at (-0.1, 0.0, 0.0)!', ')', \
'f(', '1 at pos (-0.1, 0.0, 0.0192)?', \
's(', 'f(', '1 on 0?', 'put 1 on 0!', ')', 'apply force 1!', ')', ')', \
'f(', '2 at pos (0.0, 0.0, 0.0)?', 'put 2 at (0.0, 0.0, 0.0)!', ')', ')']
start_positions = []
start_positions.append(sm.Pos(0.0, -0.05, 0.0))
start_positions.append(sm.Pos(0.0, 0.05, 0.0))
start_positions.append(sm.Pos(-0.1, 0.0, 0.0))
targets = []
targets.append(Pos(-0.1, 0.0, 0.0))
targets.append(Pos(-0.1, 0.0, 0.0192))
targets.append(Pos(0.0, 0.0, 0.0))
environment = sm_environment.Environment(start_positions,
targets,
verbose=False,
mode=sm.SMMode.BLOCKING)
n_logs = 10
gp_par.n_generations = 10
best_list_baseline_no_keep = []
gp_par.keep_baseline = False
for i in range(1, n_logs + 1):
gp_par.log_name = 'test_baseline_no_keep' + str(i)
gp.set_seeds(i)
_, _, best_fitness, _ = gp.run(environment,
gp_par,
baseline=planner_baseline)
best_list_baseline_no_keep.append(best_fitness[-1])
gp_par.n_generations = 200
best_list_baseline = []
for i in range(1, n_logs + 1):
gp_par.log_name = 'test_baseline' + str(i)
gp.set_seeds(i)
_, _, best_fitness, _ = gp.run(environment,
gp_par,
baseline=planner_baseline)
best_list_baseline.append(best_fitness[-1])
best_list_baseline_boost = []
gp_par.keep_baseline = True
gp_par.boost_baseline = True
gp_par.boost_baseline_only_co = False
for i in range(1, n_logs + 1):
gp_par.log_name = 'test_baseline_boost' + str(i)
gp.set_seeds(i)
_, _, best_fitness, _ = gp.run(environment,
gp_par,
baseline=planner_baseline)
best_list_baseline_boost.append(best_fitness[-1])
best_list_baseline_boost_only_co = []
gp_par.keep_baseline = True
gp_par.boost_baseline = True
gp_par.boost_baseline_only_co = True
for i in range(1, n_logs + 1):
gp_par.log_name = 'test_baseline_boost_only_co' + str(i)
gp.set_seeds(i)
_, _, best_fitness, _ = gp.run(environment,
gp_par,
baseline=planner_baseline)
best_list_baseline_boost_only_co.append(best_fitness[-1])
print(best_list_baseline_no_keep)
print(best_list_baseline)
print(best_list_baseline_boost)
print(best_list_baseline_boost_only_co)
print(mean(best_list_baseline_no_keep))
print(mean(best_list_baseline))
print(mean(best_list_baseline_boost))
print(mean(best_list_baseline_boost_only_co))
assert mean(best_list_baseline_boost) > mean(best_list_baseline)
assert mean(best_list_baseline_boost_only_co) > mean(
best_list_baseline_boost)
#Results after 500 gens run
#[-51.6, -89.6, -12.2, -2.3, -1.0, -50.2, -51.6, -1.0, -1.0, -1.0]
#[-12.2, -50.2, -10.8, -1.0, -1.0, -1.0, -2.2999999999999985, -0.9, -50.2, -89.6]
#[-1.0, -1.0, -1.3, -1.0, -1.0, -1.0, -50.2, -1.0, -1.0, -50.199999999999996]
#[-1.0, -1.0, -1.1, -1.0, -1.0, -1.2, -1.0, -10.8, -40.3, -1.0]
#-26.15
#-21.9
#-10.87
#-5.9
behavior_tree.load_settings_from_file(
'behavior_tree_learning/tests/BT_TEST_SETTINGS.yaml')
def test_blocking():
""" Test scenario of shuffling bricks to avoid blocking """
gp_par = gp.GpParameters()
gp_par.ind_start_length = 8
gp_par.n_population = 16
gp_par.f_crossover = 0.5
gp_par.n_offspring_crossover = 2
gp_par.replace_crossover = False
gp_par.f_mutation = 0.5
gp_par.n_offspring_mutation = 2
gp_par.parent_selection = gp.SelectionMethods.RANK
gp_par.survivor_selection = gp.SelectionMethods.RANK
gp_par.f_elites = 0.1
gp_par.f_parents = gp_par.f_elites
gp_par.mutate_co_offspring = False
gp_par.mutate_co_parents = True
gp_par.mutation_p_add = 0.4
gp_par.mutation_p_delete = 0.3
gp_par.allow_identical = False
gp_par.plot = True
gp_par.n_generations = 1000
gp_par.verbose = False
gp_par.fig_last_gen = False
behavior_tree.load_settings_from_file(
'duplo_state_machine/BT_SETTINGS_BLOCKING.yaml')
planner_baseline = ['s(', 'f(', '0 at pos (-0.1, 0.0, 0.0)?', 'put 0 at (-0.1, 0.0, 0.0)!', ')', \
'f(', '1 at pos (-0.1, 0.0, 0.0192)?', \
's(', 'f(', '1 on 0?', 'put 1 on 0!', ')', 'apply force 1!', ')', ')', \
'f(', '2 at pos (0.0, 0.0, 0.0)?', 'put 2 at (0.0, 0.0, 0.0)!', ')', ')']
solved = ['s(', 'put 1 on 0!', 'f(', '0 at pos (-0.1, 0.0, 0.0)?', 'put 2 at (0.0, 0.05, 0.0)!', ')', \
'put 0 at (-0.1, 0.0, 0.0)!', \
'f(', '1 at pos (-0.1, 0.0, 0.0192)?', 'apply force 1!', ')', \
'put 2 at (0.0, 0.0, 0.0)!', ')']
start_positions = []
start_positions.append(sm.Pos(0.0, -0.05, 0.0))
start_positions.append(sm.Pos(0.0, 0.05, 0.0))
start_positions.append(sm.Pos(-0.1, 0.0, 0.0))
targets = []
targets.append(Pos(-0.1, 0.0, 0.0))
targets.append(Pos(-0.1, 0.0, 0.0192))
targets.append(Pos(0.0, 0.0, 0.0))
environment = sm_environment.Environment(start_positions,
targets,
verbose=False,
mode=sm.SMMode.BLOCKING)
fitness = environment.get_fitness(planner_baseline)
assert fitness < -50
fitness = environment.get_fitness(solved)
assert fitness > -2
gp_par.log_name = 'test_blocking'
_, _, best_fitness, _ = gp.run(environment,
gp_par,
baseline=planner_baseline)
assert best_fitness[-1] > -2
behavior_tree.load_settings_from_file(
'behavior_tree_learning/tests/BT_TEST_SETTINGS.yaml')