def test_getitem(numpy_replay: NumpyReplayBuffer, sample_batch: SampleBatch,
idx):
replay = numpy_replay
batch = replay[idx]
assert isinstance(batch, dict)
assert all([
np.allclose(batch[k], sample_batch[k][idx])
for k in sample_batch.keys()
])
mean = np.mean(sample_batch[SampleBatch.CUR_OBS], axis=0)
std = np.std(sample_batch[SampleBatch.CUR_OBS], axis=0)
replay.update_obs_stats()
batch = replay[idx]
for key in SampleBatch.CUR_OBS, SampleBatch.NEXT_OBS:
expected = (sample_batch[key][idx] - mean) / (std + 1e-7)
assert np.allclose(batch[key], expected)
def test_getitem(filled_replay: NumpyReplayBuffer, sample_batch: SampleBatch,
idx):
replay = filled_replay
batch = replay[idx]
assert isinstance(batch, dict)
assert all([
np.allclose(batch[k], sample_batch[k][idx])
for k in sample_batch.keys()
])
mean = np.mean(sample_batch[SampleBatch.CUR_OBS], axis=0)
std = np.std(sample_batch[SampleBatch.CUR_OBS], axis=0)
std[std < 1e-12] = 1.0
replay.compute_stats = True
batch = replay[idx]
for key in SampleBatch.CUR_OBS, SampleBatch.NEXT_OBS:
expected = (sample_batch[key][idx] - mean) / std
assert np.allclose(batch[key], expected)