Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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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.communicator_objects.agent_info_action_pair_pb2 import (
AgentInfoActionPairProto,
)
from mlagents_envs.communicator_objects.agent_action_pb2 import AgentActionProto
from mlagents_envs.base_env import (
BehaviorSpec,
ActionSpec,
SensorType,
DecisionSteps,
TerminalSteps,
)
from mlagents_envs.exception import UnityObservationException
from mlagents_envs.rpc_utils import (
behavior_spec_from_proto,
process_pixels,
_process_visual_observation,
_process_vector_observation,
steps_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 % 4 == 0
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)
bytes_out = bytes()
num_channels = in_array.shape[2]
num_images = (num_channels + 2) // 3
# Split the input image into batches of 3 channels.
for i in range(num_images):
sub_image = image_arr[..., 3 * i : 3 * i + 3]
if (i == num_images - 1) and (num_channels % 3) != 0:
# Pad zeros
zero_shape = list(in_array.shape)
zero_shape[2] = 3 - (num_channels % 3)
z = np.zeros(zero_shape, dtype=np.uint8)
sub_image = np.concatenate([sub_image, z], axis=2)
im = Image.fromarray(sub_image, "RGB")
byteIO = io.BytesIO()
im.save(byteIO, format="PNG")
bytes_out += byteIO.getvalue()
return bytes_out
# test helper function for old C# API (no compressed channel mapping)
def generate_compressed_proto_obs(
in_array: np.ndarray, grayscale: bool = False
) -> ObservationProto:
obs_proto = ObservationProto()
obs_proto.compressed_data = generate_compressed_data(in_array)
obs_proto.compression_type = PNG
if grayscale:
# grayscale flag is only used for old API without mapping
expected_shape = [in_array.shape[0], in_array.shape[1], 1]
obs_proto.shape.extend(expected_shape)
else:
obs_proto.shape.extend(in_array.shape)
return obs_proto
# test helper function for new C# API (with compressed channel mapping)
def generate_compressed_proto_obs_with_mapping(
in_array: np.ndarray, mapping: List[int]
) -> ObservationProto:
obs_proto = ObservationProto()
obs_proto.compressed_data = generate_compressed_data(in_array)
obs_proto.compression_type = PNG
if mapping is not None:
obs_proto.compressed_channel_mapping.extend(mapping)
expected_shape = [
in_array.shape[0],
in_array.shape[1],
len({m for m in mapping if m >= 0}),
]
obs_proto.shape.extend(expected_shape)
else:
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 proto_from_steps(
decision_steps: DecisionSteps, terminal_steps: TerminalSteps
) -> List[AgentInfoProto]:
agent_info_protos: List[AgentInfoProto] = []
# Take care of the DecisionSteps first
for agent_id in decision_steps.agent_id:
agent_id_index = decision_steps.agent_id_to_index[agent_id]
reward = decision_steps.reward[agent_id_index]
done = False
max_step_reached = False
agent_mask = None
if decision_steps.action_mask is not None:
agent_mask = [] # type: ignore
for _branch in decision_steps.action_mask:
agent_mask = np.concatenate(
(agent_mask, _branch[agent_id_index, :]), axis=0
)
observations: List[ObservationProto] = []
for all_observations_of_type in decision_steps.obs:
observation = all_observations_of_type[agent_id_index]
if len(observation.shape) == 3:
observations.append(generate_uncompressed_proto_obs(observation))
else:
observations.append(
ObservationProto(
float_data=ObservationProto.FloatData(data=observation),
shape=[len(observation)],
compression_type=NONE,
)
)
agent_info_proto = AgentInfoProto(
reward=reward,
done=done,
id=agent_id,
max_step_reached=max_step_reached,
action_mask=agent_mask,
observations=observations,
)
agent_info_protos.append(agent_info_proto)
# Take care of the TerminalSteps second
for agent_id in terminal_steps.agent_id:
agent_id_index = terminal_steps.agent_id_to_index[agent_id]
reward = terminal_steps.reward[agent_id_index]
done = True
max_step_reached = terminal_steps.interrupted[agent_id_index]
final_observations: List[ObservationProto] = []
for all_observations_of_type in terminal_steps.obs:
observation = all_observations_of_type[agent_id_index]
if len(observation.shape) == 3:
final_observations.append(generate_uncompressed_proto_obs(observation))
else:
final_observations.append(
ObservationProto(
float_data=ObservationProto.FloatData(data=observation),
shape=[len(observation)],
compression_type=NONE,
)
)
agent_info_proto = AgentInfoProto(
reward=reward,
done=done,
id=agent_id,
max_step_reached=max_step_reached,
action_mask=None,
observations=final_observations,
)
agent_info_protos.append(agent_info_proto)
return agent_info_protos
# The arguments here are the DecisionSteps, TerminalSteps and continuous/discrete actions for a single agent name
def proto_from_steps_and_action(
decision_steps: DecisionSteps,
terminal_steps: TerminalSteps,
continuous_actions: np.ndarray,
discrete_actions: np.ndarray,
) -> List[AgentInfoActionPairProto]:
agent_info_protos = proto_from_steps(decision_steps, terminal_steps)
agent_action_protos = []
num_agents = (
len(continuous_actions)
if continuous_actions is not None
else len(discrete_actions)
)
for i in range(num_agents):
proto = AgentActionProto()
if continuous_actions is not None:
proto.continuous_actions.extend(continuous_actions[i])
proto.vector_actions_deprecated.extend(continuous_actions[i])
if discrete_actions is not None:
proto.discrete_actions.extend(discrete_actions[i])
proto.vector_actions_deprecated.extend(discrete_actions[i])
agent_action_protos.append(proto)
agent_info_action_pair_protos = [
AgentInfoActionPairProto(agent_info=agent_info_proto, action_info=action_proto)
for agent_info_proto, action_proto in zip(
agent_info_protos, agent_action_protos
)
]
return agent_info_action_pair_protos
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, 3)
assert out_array.shape == (128, 64, 3)
assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01
assert np.allclose(in_array, out_array, atol=0.01)
def test_process_pixels_multi_png():
height = 128
width = 64
num_channels = 7
in_array = np.random.rand(height, width, num_channels)
byte_arr = generate_compressed_data(in_array)
out_array = process_pixels(byte_arr, num_channels)
assert out_array.shape == (height, width, num_channels)
assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01
assert np.allclose(in_array, out_array, atol=0.01)
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, 1)
assert out_array.shape == (128, 64, 1)
assert np.mean(in_array.mean(axis=2, keepdims=True) - out_array) < 0.01
assert np.allclose(in_array.mean(axis=2, keepdims=True), out_array, atol=0.01)
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.allclose(arr, 0.1, atol=0.01)
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)
in_array_2_mapping = [0, 1, 2]
proto_obs_2 = generate_compressed_proto_obs_with_mapping(
in_array_2, in_array_2_mapping
)
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 np.allclose(arr[0, :, :, :], in_array_1, atol=0.01)
assert np.allclose(arr[1, :, :, :], in_array_2, atol=0.01)
def test_process_visual_observation_grayscale():
in_array_1 = np.random.rand(128, 64, 3)
proto_obs_1 = generate_compressed_proto_obs(in_array_1, grayscale=True)
expected_out_array_1 = np.mean(in_array_1, axis=2, keepdims=True)
in_array_2 = np.random.rand(128, 64, 3)
in_array_2_mapping = [0, 0, 0]
proto_obs_2 = generate_compressed_proto_obs_with_mapping(
in_array_2, in_array_2_mapping
)
expected_out_array_2 = np.mean(in_array_2, axis=2, keepdims=True)
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, 1), ap_list)
assert list(arr.shape) == [2, 128, 64, 1]
assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
assert np.allclose(arr[1, :, :, :], expected_out_array_2, atol=0.01)
def test_process_visual_observation_padded_channels():
in_array_1 = np.random.rand(128, 64, 12)
in_array_1_mapping = [0, 1, 2, 3, -1, -1, 4, 5, 6, 7, -1, -1]
proto_obs_1 = generate_compressed_proto_obs_with_mapping(
in_array_1, in_array_1_mapping
)
expected_out_array_1 = np.take(in_array_1, [0, 1, 2, 3, 6, 7, 8, 9], axis=2)
ap1 = AgentInfoProto()
ap1.observations.extend([proto_obs_1])
ap_list = [ap1]
arr = _process_visual_observation(0, (128, 64, 8), ap_list)
assert list(arr.shape) == [1, 128, 64, 8]
assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
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,)]
sensor_type = [SensorType.OBSERVATION, SensorType.OBSERVATION]
spec = BehaviorSpec(shapes, sensor_type, ActionSpec.create_continuous(3))
ap_list = generate_list_agent_proto(n_agents, shapes)
decision_steps, terminal_steps = steps_from_proto(ap_list, spec)
for agent_id in range(n_agents):
if agent_id in decision_steps:
# we set the reward equal to the agent id in generate_list_agent_proto
assert decision_steps[agent_id].reward == agent_id
elif agent_id in terminal_steps:
assert terminal_steps[agent_id].reward == agent_id
else:
raise Exception("Missing agent from the steps")
# We sort the AgentId since they are split between DecisionSteps and TerminalSteps
combined_agent_id = list(decision_steps.agent_id) + list(terminal_steps.agent_id)
combined_agent_id.sort()
assert combined_agent_id == list(range(n_agents))
for agent_id in range(n_agents):
assert (agent_id in terminal_steps) == (agent_id % 2 == 0)
if agent_id in terminal_steps:
assert terminal_steps[agent_id].interrupted == (agent_id % 4 == 0)
assert decision_steps.obs[0].shape[1] == shapes[0][0]
assert decision_steps.obs[1].shape[1] == shapes[1][0]
assert terminal_steps.obs[0].shape[1] == shapes[0][0]
assert terminal_steps.obs[1].shape[1] == shapes[1][0]
def test_action_masking_discrete():
n_agents = 10
shapes = [(3,), (4,)]
sensor_type = [SensorType.OBSERVATION, SensorType.OBSERVATION]
behavior_spec = BehaviorSpec(
shapes, sensor_type, ActionSpec.create_discrete((7, 3))
)
ap_list = generate_list_agent_proto(n_agents, shapes)
decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
masks = decision_steps.action_mask
assert isinstance(masks, list)
assert len(masks) == 2
assert masks[0].shape == (n_agents / 2, 7) # half agents are done
assert masks[1].shape == (n_agents / 2, 3) # half agents are done
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,)]
sensor_type = [SensorType.OBSERVATION, SensorType.OBSERVATION]
behavior_spec = BehaviorSpec(shapes, sensor_type, ActionSpec.create_discrete((10,)))
ap_list = generate_list_agent_proto(n_agents, shapes)
decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
masks = decision_steps.action_mask
assert isinstance(masks, list)
assert len(masks) == 1
assert masks[0].shape == (n_agents / 2, 10)
assert masks[0][0, 0]
def test_action_masking_discrete_2():
n_agents = 10
shapes = [(3,), (4,)]
sensor_type = [SensorType.OBSERVATION, SensorType.OBSERVATION]
behavior_spec = BehaviorSpec(
shapes, sensor_type, ActionSpec.create_discrete((2, 2, 6))
)
ap_list = generate_list_agent_proto(n_agents, shapes)
decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
masks = decision_steps.action_mask
assert isinstance(masks, list)
assert len(masks) == 3
assert masks[0].shape == (n_agents / 2, 2)
assert masks[1].shape == (n_agents / 2, 2)
assert masks[2].shape == (n_agents / 2, 6)
assert masks[0][0, 0]
def test_action_masking_continuous():
n_agents = 10
shapes = [(3,), (4,)]
sensor_type = [SensorType.OBSERVATION, SensorType.OBSERVATION]
behavior_spec = BehaviorSpec(shapes, sensor_type, ActionSpec.create_continuous(10))
ap_list = generate_list_agent_proto(n_agents, shapes)
decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
masks = decision_steps.action_mask
assert masks is None
def test_agent_behavior_spec_from_proto():
agent_proto = generate_list_agent_proto(1, [(3,), (4,)])[0]
bp = BrainParametersProto()
bp.vector_action_size_deprecated.extend([5, 4])
bp.vector_action_space_type_deprecated = 0
behavior_spec = behavior_spec_from_proto(bp, agent_proto)
assert behavior_spec.action_spec.is_discrete()
assert not behavior_spec.action_spec.is_continuous()
assert behavior_spec.observation_shapes == [(3,), (4,)]
assert behavior_spec.action_spec.discrete_branches == (5, 4)
assert behavior_spec.action_spec.discrete_size == 2
bp = BrainParametersProto()
bp.vector_action_size_deprecated.extend([6])
bp.vector_action_space_type_deprecated = 1
behavior_spec = behavior_spec_from_proto(bp, agent_proto)
assert not behavior_spec.action_spec.is_discrete()
assert behavior_spec.action_spec.is_continuous()
assert behavior_spec.action_spec.continuous_size == 6
def test_batched_step_result_from_proto_raises_on_infinite():
n_agents = 10
shapes = [(3,), (4,)]
sensor_type = [SensorType.OBSERVATION, SensorType.OBSERVATION]
behavior_spec = BehaviorSpec(shapes, sensor_type, ActionSpec.create_continuous(3))
ap_list = generate_list_agent_proto(n_agents, shapes, infinite_rewards=True)
with pytest.raises(RuntimeError):
steps_from_proto(ap_list, behavior_spec)
def test_batched_step_result_from_proto_raises_on_nan():
n_agents = 10
shapes = [(3,), (4,)]
sensor_type = [SensorType.OBSERVATION, SensorType.OBSERVATION]
behavior_spec = BehaviorSpec(shapes, sensor_type, ActionSpec.create_continuous(3))
ap_list = generate_list_agent_proto(n_agents, shapes, nan_observations=True)
with pytest.raises(RuntimeError):
steps_from_proto(ap_list, behavior_spec)