import numpy as np from mlagents.trainers.buffer import Buffer def assert_array(a, b): assert a.shape == b.shape la = list(a.flatten()) lb = list(b.flatten()) for i in range(len(la)): assert la[i] == lb[i] def test_buffer(): b = Buffer() for fake_agent_id in range(4): for step in range(9): b[fake_agent_id]["vector_observation"].append( [ 100 * fake_agent_id + 10 * step + 1, 100 * fake_agent_id + 10 * step + 2, 100 * fake_agent_id + 10 * step + 3, ] ) b[fake_agent_id]["action"].append( [ 100 * fake_agent_id + 10 * step + 4, 100 * fake_agent_id + 10 * step + 5, ] ) a = b[1]["vector_observation"].get_batch( batch_size=2, training_length=1, sequential=True ) assert_array(a, np.array([[171, 172, 173], [181, 182, 183]])) a = b[2]["vector_observation"].get_batch( batch_size=2, training_length=3, sequential=True ) assert_array( a, np.array( [ [[231, 232, 233], [241, 242, 243], [251, 252, 253]], [[261, 262, 263], [271, 272, 273], [281, 282, 283]], ] ), ) a = b[2]["vector_observation"].get_batch( batch_size=2, training_length=3, sequential=False ) assert_array( a, np.array( [ [[251, 252, 253], [261, 262, 263], [271, 272, 273]], [[261, 262, 263], [271, 272, 273], [281, 282, 283]], ] ), ) b[4].reset_agent() assert len(b[4]) == 0 b.append_update_buffer(3, batch_size=None, training_length=2) b.append_update_buffer(2, batch_size=None, training_length=2) assert len(b.update_buffer["action"]) == 10 assert np.array(b.update_buffer["action"]).shape == (10, 2, 2) c = b.update_buffer.make_mini_batch(start=0, end=1) assert c.keys() == b.update_buffer.keys() assert c["action"].shape == (1, 2, 2)