def test_per_buffer_too_few_samples():
# Assign
batch_size = 5
per_buffer = PERBuffer(batch_size, 10)
# Act & Assert
for _ in range(batch_size):
assert per_buffer.sample_list() is None
per_buffer.add(priority=0.1, reward=0.1)
assert per_buffer.sample_list() is not None
def test_per_buffer_reset_alpha():
# Assign
per_buffer = PERBuffer(10, 10, alpha=0.1)
for _ in range(30):
per_buffer.add(reward=np.random.randint(0, 1e5),
priority=np.random.random())
# Act
old_experiences = per_buffer.sample_list()
per_buffer.reset_alpha(0.5)
new_experiences = per_buffer.sample_list()
# Assert
assert old_experiences is not None and new_experiences is not None
sorted_new_experiences = sorted(new_experiences, key=lambda k: k.index)
sorted_old_experiences = sorted(old_experiences, key=lambda k: k.index)
for (new_sample, old_sample) in zip(sorted_new_experiences,
sorted_old_experiences):
assert new_sample.index == old_sample.index
assert new_sample.weight != old_sample.weight
assert new_sample.reward == old_sample.reward
def test_per_buffer_add_one_sample_one():
# Assign
per_buffer = PERBuffer(1, 20)
# Act
per_buffer.add(priority=0.5, state=range(5))
# Assert
raw_samples = per_buffer.sample_list()
assert raw_samples is not None
experience = raw_samples[0]
assert experience.state == range(5)
assert experience.weight == 1. # max scale
assert experience.index == 0
def test_per_buffer_priority_update():
"""Update all priorities to the same value makes them all to be 1."""
# Assign
batch_size = 5
buffer_size = 10
per_buffer = PERBuffer(batch_size, buffer_size)
for _ in range(2 * buffer_size): # Make sure we fill the whole buffer
per_buffer.add(priority=np.random.randint(10),
state=np.random.random(10))
per_buffer.add(priority=100,
state=np.random.random(10)) # Make sure there's one highest
# Act & Assert
experiences = per_buffer.sample_list(beta=0.5)
assert experiences is not None
assert sum([exp.weight for exp in experiences]) < batch_size
per_buffer.priority_update(indices=range(buffer_size),
priorities=np.ones(buffer_size))
experiences = per_buffer.sample_list(beta=0.9)
assert experiences is not None
weights = [exp.weight for exp in experiences]
assert sum(weights) == batch_size
assert all([w == 1 for w in weights])
def test_per_buffer_add_two_sample_two_beta():
# Assign
per_buffer = PERBuffer(2, 20)
# Act
per_buffer.add(state=range(5), priority=0.9)
per_buffer.add(state=range(3, 8), priority=0.1)
# Assert
experiences = per_buffer.sample_list(beta=0.6)
assert experiences is not None
for experience in experiences:
if experience.index == 0:
assert experience.state == range(5)
# assert 0.936 < experience.weight < 0.937
assert 0.946 < experience.weight < 0.947
else:
assert experience.state == range(3, 8)
assert experience.weight == 1.
def test_per_buffer_sample():
# Assign
buffer_size = 5
per_buffer = PERBuffer(buffer_size)
# Act
for priority in range(buffer_size):
state = np.arange(priority, priority + 10)
per_buffer.add(priority=priority + 0.01, state=state)
# Assert
experiences = per_buffer.sample_list()
assert experiences is not None
assert len(experiences) == buffer_size
zipped_exp = [(exp.state, exp.reward, exp.weight, exp.index)
for exp in experiences]
states, rewards, weights, indices = zip(*zipped_exp)
assert len(weights) == len(indices) == buffer_size
assert all([s is not None for s in states])
assert all([r is None for r in rewards])