import io import numpy as np import pytest from typing import List, Tuple from mlagents_envs.communicator_objects.agent_info_pb2 import AgentInfoProto from mlagents_envs.communicator_objects.observation_pb2 import ( ObservationProto, NONE, PNG, ) from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto from mlagents_envs.base_env import AgentGroupSpec, ActionType from mlagents_envs.exception import UnityObservationException from mlagents_envs.rpc_utils import ( agent_group_spec_from_proto, process_pixels, _process_visual_observation, _process_vector_observation, batched_step_result_from_proto, ) from PIL import Image def generate_list_agent_proto( n_agent: int, shape: List[Tuple[int]], infinite_rewards: bool = False, nan_observations: bool = False, ) -> List[AgentInfoProto]: result = [] for agent_index in range(n_agent): ap = AgentInfoProto() ap.reward = float("inf") if infinite_rewards else agent_index ap.done = agent_index % 2 == 0 ap.max_step_reached = agent_index % 2 == 1 ap.id = agent_index ap.action_mask.extend([True, False] * 5) obs_proto_list = [] for obs_index in range(len(shape)): obs_proto = ObservationProto() obs_proto.shape.extend(list(shape[obs_index])) obs_proto.compression_type = NONE obs_proto.float_data.data.extend( ([float("nan")] if nan_observations else [0.1]) * np.prod(shape[obs_index]) ) obs_proto_list.append(obs_proto) ap.observations.extend(obs_proto_list) result.append(ap) return result def generate_compressed_data(in_array: np.ndarray) -> bytes: image_arr = (in_array * 255).astype(np.uint8) im = Image.fromarray(image_arr, "RGB") byteIO = io.BytesIO() im.save(byteIO, format="PNG") return byteIO.getvalue() def generate_compressed_proto_obs(in_array: np.ndarray) -> ObservationProto: obs_proto = ObservationProto() obs_proto.compressed_data = generate_compressed_data(in_array) obs_proto.compression_type = PNG obs_proto.shape.extend(in_array.shape) return obs_proto def generate_uncompressed_proto_obs(in_array: np.ndarray) -> ObservationProto: obs_proto = ObservationProto() obs_proto.float_data.data.extend(in_array.flatten().tolist()) obs_proto.compression_type = NONE obs_proto.shape.extend(in_array.shape) return obs_proto def test_process_pixels(): in_array = np.random.rand(128, 64, 3) byte_arr = generate_compressed_data(in_array) out_array = process_pixels(byte_arr, False) assert out_array.shape == (128, 64, 3) assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01 assert (in_array - out_array < 0.01).all() def test_process_pixels_gray(): in_array = np.random.rand(128, 64, 3) byte_arr = generate_compressed_data(in_array) out_array = process_pixels(byte_arr, True) assert out_array.shape == (128, 64, 1) assert np.mean(in_array.mean(axis=2, keepdims=True) - out_array) < 0.01 assert (in_array.mean(axis=2, keepdims=True) - out_array < 0.01).all() def test_vector_observation(): n_agents = 10 shapes = [(3,), (4,)] list_proto = generate_list_agent_proto(n_agents, shapes) for obs_index, shape in enumerate(shapes): arr = _process_vector_observation(obs_index, shape, list_proto) assert list(arr.shape) == ([n_agents] + list(shape)) assert (np.abs(arr - 0.1) < 0.01).all() def test_process_visual_observation(): in_array_1 = np.random.rand(128, 64, 3) proto_obs_1 = generate_compressed_proto_obs(in_array_1) in_array_2 = np.random.rand(128, 64, 3) proto_obs_2 = generate_uncompressed_proto_obs(in_array_2) ap1 = AgentInfoProto() ap1.observations.extend([proto_obs_1]) ap2 = AgentInfoProto() ap2.observations.extend([proto_obs_2]) ap_list = [ap1, ap2] arr = _process_visual_observation(0, (128, 64, 3), ap_list) assert list(arr.shape) == [2, 128, 64, 3] assert (arr[0, :, :, :] - in_array_1 < 0.01).all() assert (arr[1, :, :, :] - in_array_2 < 0.01).all() def test_process_visual_observation_bad_shape(): in_array_1 = np.random.rand(128, 64, 3) proto_obs_1 = generate_compressed_proto_obs(in_array_1) ap1 = AgentInfoProto() ap1.observations.extend([proto_obs_1]) ap_list = [ap1] with pytest.raises(UnityObservationException): _process_visual_observation(0, (128, 42, 3), ap_list) def test_batched_step_result_from_proto(): n_agents = 10 shapes = [(3,), (4,)] group_spec = AgentGroupSpec(shapes, ActionType.CONTINUOUS, 3) ap_list = generate_list_agent_proto(n_agents, shapes) result = batched_step_result_from_proto(ap_list, group_spec) assert list(result.reward) == list(range(n_agents)) assert list(result.agent_id) == list(range(n_agents)) for index in range(n_agents): assert result.done[index] == (index % 2 == 0) assert result.max_step[index] == (index % 2 == 1) assert list(result.obs[0].shape) == [n_agents] + list(shapes[0]) assert list(result.obs[1].shape) == [n_agents] + list(shapes[1]) def test_action_masking_discrete(): n_agents = 10 shapes = [(3,), (4,)] group_spec = AgentGroupSpec(shapes, ActionType.DISCRETE, (7, 3)) ap_list = generate_list_agent_proto(n_agents, shapes) result = batched_step_result_from_proto(ap_list, group_spec) masks = result.action_mask assert isinstance(masks, list) assert len(masks) == 2 assert masks[0].shape == (n_agents, 7) assert masks[1].shape == (n_agents, 3) assert masks[0][0, 0] assert not masks[1][0, 0] assert masks[1][0, 1] def test_action_masking_discrete_1(): n_agents = 10 shapes = [(3,), (4,)] group_spec = AgentGroupSpec(shapes, ActionType.DISCRETE, (10,)) ap_list = generate_list_agent_proto(n_agents, shapes) result = batched_step_result_from_proto(ap_list, group_spec) masks = result.action_mask assert isinstance(masks, list) assert len(masks) == 1 assert masks[0].shape == (n_agents, 10) assert masks[0][0, 0] def test_action_masking_discrete_2(): n_agents = 10 shapes = [(3,), (4,)] group_spec = AgentGroupSpec(shapes, ActionType.DISCRETE, (2, 2, 6)) ap_list = generate_list_agent_proto(n_agents, shapes) result = batched_step_result_from_proto(ap_list, group_spec) masks = result.action_mask assert isinstance(masks, list) assert len(masks) == 3 assert masks[0].shape == (n_agents, 2) assert masks[1].shape == (n_agents, 2) assert masks[2].shape == (n_agents, 6) assert masks[0][0, 0] def test_action_masking_continuous(): n_agents = 10 shapes = [(3,), (4,)] group_spec = AgentGroupSpec(shapes, ActionType.CONTINUOUS, 10) ap_list = generate_list_agent_proto(n_agents, shapes) result = batched_step_result_from_proto(ap_list, group_spec) masks = result.action_mask assert masks is None def test_agent_group_spec_from_proto(): agent_proto = generate_list_agent_proto(1, [(3,), (4,)])[0] bp = BrainParametersProto() bp.vector_action_size.extend([5, 4]) bp.vector_action_space_type = 0 group_spec = agent_group_spec_from_proto(bp, agent_proto) assert group_spec.is_action_discrete() assert not group_spec.is_action_continuous() assert group_spec.observation_shapes == [(3,), (4,)] assert group_spec.discrete_action_branches == (5, 4) assert group_spec.action_size == 2 bp = BrainParametersProto() bp.vector_action_size.extend([6]) bp.vector_action_space_type = 1 group_spec = agent_group_spec_from_proto(bp, agent_proto) assert not group_spec.is_action_discrete() assert group_spec.is_action_continuous() assert group_spec.action_size == 6 def test_batched_step_result_from_proto_raises_on_infinite(): n_agents = 10 shapes = [(3,), (4,)] group_spec = AgentGroupSpec(shapes, ActionType.CONTINUOUS, 3) ap_list = generate_list_agent_proto(n_agents, shapes, infinite_rewards=True) with pytest.raises(RuntimeError): batched_step_result_from_proto(ap_list, group_spec) def test_batched_step_result_from_proto_raises_on_nan(): n_agents = 10 shapes = [(3,), (4,)] group_spec = AgentGroupSpec(shapes, ActionType.CONTINUOUS, 3) ap_list = generate_list_agent_proto(n_agents, shapes, nan_observations=True) with pytest.raises(RuntimeError): batched_step_result_from_proto(ap_list, group_spec)