def test_buffer_truncate():
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
update_buffer.truncate(2)
assert update_buffer.num_experiences == 2
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 LSTM, truncate should be some multiple of sequence_length
update_buffer.truncate(4, sequence_length=3)
assert update_buffer.num_experiences == 3
for buffer_field in update_buffer.values():
assert isinstance(buffer_field, AgentBufferField)
def evaluate_batch(self, mini_batch: AgentBuffer) -> RewardSignalResult:
"""
Evaluates the reward for the data present in the Dict mini_batch. Use this when evaluating a reward
function drawn straight from a Buffer.
:param mini_batch: A Dict of numpy arrays (the format used by our Buffer)
when drawing from the update buffer.
:return: a RewardSignalResult of (scaled intrinsic reward, unscaled intrinsic reward) provided by the generator
"""
mini_batch_len = len(next(iter(mini_batch.values())))
return RewardSignalResult(
self.strength * np.zeros(mini_batch_len, dtype=np.float32),
np.zeros(mini_batch_len, dtype=np.float32),
)
