def test_sample_empty_buffer(rank):
world = get_world()
default_logger.info(f"{rank} started")
np.random.seed(0)
group = world.create_rpc_group("group", ["0", "1", "2"])
buffer = DistributedPrioritizedBuffer("buffer", group, 5)
if rank in (0, 1):
sleep(5)
else:
sleep(2)
batch_size, sample, indexes, priorities = buffer.sample_batch(
10, sample_attrs=["index"])
assert batch_size == 0
assert sample is None
assert indexes is None
assert priorities is None
return True
def test_append_sample_controlled(rank, trans_list):
world = get_world()
default_logger.info("{} started".format(rank))
np.random.seed(0)
group = world.create_rpc_group("group", ["0", "1", "2"])
buffer = DistributedPrioritizedBuffer("buffer", group, 5)
if rank in (0, 1):
for i in range(5):
trans, prior = trans_list[i]
buffer.append(trans, prior)
sleep(5)
else:
sleep(2)
batch_size, sample, indexes, priorities = \
buffer.sample_batch(10, sample_attrs=["index"])
default_logger.info("sampled batch size: {}".format(batch_size))
default_logger.info(sample)
default_logger.info(indexes)
default_logger.info(priorities)
assert batch_size == 10
assert sample[0] == [0, 1, 2, 2, 4, 0, 1, 2, 2, 4]
assert list(indexes.keys()) == ["0", "1"]
assert np.all(
np.abs(priorities - [
0.75316421, 0.75316421, 0.75316421, 0.75316421, 1.0,
0.75316421, 0.75316421, 0.75316421, 0.75316421, 1.0
]) < 1e-6)
buffer.update_priority(priorities, indexes)
return True
def test_append_sample_empty(rank, trans_list):
world = get_world()
default_logger.info(f"{rank} started")
np.random.seed(0)
group = world.create_rpc_group("group", ["0", "1", "2"])
buffer = DistributedPrioritizedBuffer("buffer", group, 5)
if rank in (0, 1):
for i in range(5):
trans, prior = trans_list[i]
buffer.append(trans)
sleep(5)
else:
sleep(2)
batch_size, sample, indexes, priorities = buffer.sample_batch(0)
assert batch_size == 0
assert sample is None
assert indexes is None
assert priorities is None
return True
def test_append_size(rank, trans_list):
world = get_world()
default_logger.info(f"{rank} started")
np.random.seed(0)
group = world.create_rpc_group("group", ["0", "1", "2"])
buffer = DistributedPrioritizedBuffer("buffer", group, 5)
if rank in (0, 1):
if rank == 0:
for i in range(5):
data, prior = trans_list[i]
buffer.append(data, prior)
assert buffer.size() == 5
else:
assert buffer.size() == 0
sleep(5)
else:
sleep(2)
assert buffer.size() == 0
assert buffer.all_size() == 5
return True
def test_append_sample_random(rank, trans_list):
world = get_world()
count = 0
default_logger.info("{} started".format(rank))
group = world.create_rpc_group("group", ["0", "1", "2"])
buffer = DistributedPrioritizedBuffer("buffer", group, 5)
if rank in (0, 1):
begin = time()
while time() - begin < 10:
trans, prior = random.choice(trans_list)
buffer.append(trans)
default_logger.info("{} append {} success".format(rank, count))
count += 1
sleep(random.random() * 0.5)
else:
sleep(5)
begin = time()
while time() - begin < 5:
batch_size, sample, indexes, priorities = \
buffer.sample_batch(10)
default_logger.info(
"sampled batch size: {}".format(batch_size))
assert batch_size > 0
# state
assert (list(sample[0]["state_1"].shape) == [batch_size, 2])
# action
assert (list(sample[1]["action_1"].shape) == [batch_size, 3])
# next state
assert (list(
sample[2]["next_state_1"].shape) == [batch_size, 2])
# reward
assert list(sample[3].shape) == [batch_size, 1]
# terminal
assert list(sample[4].shape) == [batch_size, 1]
# index
assert len(sample[5]) == batch_size
# simulate perform a backward process
sleep(1)
buffer.update_priority(priorities, indexes)
default_logger.info("{} sample {} success".format(rank, count))
count += 1
sleep(1)
return True
def test_append_clear(rank, trans_list):
world = get_world()
default_logger.info("{} started".format(rank))
np.random.seed(0)
group = world.create_rpc_group("group", ["0", "1", "2"])
buffer = DistributedPrioritizedBuffer("buffer", group, 5)
if rank in (0, 1):
for i in range(5):
trans, prior = trans_list[i]
buffer.append(trans, prior)
if rank == 0:
buffer.clear()
assert buffer.size() == 0
sleep(5)
else:
sleep(2)
assert buffer.all_size() == 5
buffer.all_clear()
assert buffer.all_size() == 0
return True
