def test_pickle_controller():
names = ["CMAESOptimizer", "REPSOptimizer"]
optimizers = [CMAESOptimizer, REPSOptimizer]
for name, Optimizer in zip(names, optimizers):
opt = Optimizer(initial_params=np.zeros(2))
ctrl = Controller(environment=ObjectiveFunction(),
behavior_search=JustOptimizer(opt))
assert_pickle(name, ctrl)
def test_save_black_box_search():
bs = BlackBoxSearch(ConstantBehavior(), NoOptimizer())
bs.init(5, 5)
assert_pickle("BlackBoxSearch", bs)
path = "." + os.sep
bs.write_results(path)
bs.get_behavior_from_results(path)
filename = path + "BlackBoxSearch.pickle"
assert_true(os.path.exists(filename))
if os.path.exists(filename):
os.remove(filename)
def test_optimizers_follow_standard_protocol():
for name, Optimizer in ALL_OPTIMIZERS:
opt = Optimizer()
n_params = 2
opt.init(n_params)
assert_false(opt.is_behavior_learning_done())
params = np.empty(n_params)
opt.get_next_parameters(params)
assert_true(np.isfinite(params).all())
opt.set_evaluation_feedback(np.array([0.0]))
params = opt.get_best_parameters()
assert_equal(len(params), n_params)
assert_true(np.isfinite(params).all())
assert_pickle(name, opt)
def test_contextual_optimizers_follow_standard_protocol():
for name, ContextualOptimizer in ALL_CONTEXTUALOPTIMIZERS:
opt = ContextualOptimizer()
n_params = 1
n_context_dims = 1
opt.init(n_params, n_context_dims)
context = opt.get_desired_context()
if context is None:
context = np.zeros(n_context_dims)
opt.set_context(context)
assert_false(opt.is_behavior_learning_done())
params = np.empty(n_params)
opt.get_next_parameters(params)
assert_true(np.isfinite(params).all())
opt.set_evaluation_feedback(np.array([0.0]))
policy = opt.best_policy()
assert_true(np.isfinite(policy(context)).all())
assert_pickle(name, opt)