def test_buffer_sample():
agent_1_buffer = construct_fake_buffer(1)
agent_2_buffer = construct_fake_buffer(2)
update_buffer = AgentBuffer()
agent_1_buffer.resequence_and_append(update_buffer,
batch_size=None,
training_length=2)
agent_2_buffer.resequence_and_append(update_buffer,
batch_size=None,
training_length=2)
# Test non-LSTM
mb = update_buffer.sample_mini_batch(batch_size=4, sequence_length=1)
assert mb.keys() == update_buffer.keys()
assert np.array(mb[BufferKey.CONTINUOUS_ACTION]).shape == (4, 2)
# Test LSTM
# We need to check if we ever get a breaking start - this will maximize the probability
mb = update_buffer.sample_mini_batch(batch_size=20, sequence_length=19)
assert mb.keys() == update_buffer.keys()
# Should only return one sequence
assert np.array(mb[BufferKey.CONTINUOUS_ACTION]).shape == (19, 2)
def test_buffer_sample():
b = construct_fake_processing_buffer()
update_buffer = AgentBuffer()
b.append_to_update_buffer(update_buffer,
3,
batch_size=None,
training_length=2)
b.append_to_update_buffer(update_buffer,
2,
batch_size=None,
training_length=2)
# Test non-LSTM
mb = update_buffer.sample_mini_batch(batch_size=4, sequence_length=1)
assert mb.keys() == update_buffer.keys()
assert np.array(mb["action"]).shape == (4, 2)
# Test LSTM
# We need to check if we ever get a breaking start - this will maximize the probability
mb = update_buffer.sample_mini_batch(batch_size=20, sequence_length=19)
assert mb.keys() == update_buffer.keys()
# Should only return one sequence
assert np.array(mb["action"]).shape == (19, 2)
