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 os
from typing import List, Tuple
import numpy as np
from mlagents.trainers.buffer import AgentBuffer
from mlagents_envs.communicator_objects.agent_info_action_pair_pb2 import (
AgentInfoActionPairProto,
)
from mlagents.trainers.trajectory import SplitObservations
from mlagents_envs.rpc_utils import behavior_spec_from_proto, steps_from_proto
from mlagents_envs.base_env import BehaviorSpec
from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
from mlagents_envs.communicator_objects.demonstration_meta_pb2 import (
DemonstrationMetaProto,
)
from mlagents_envs.timers import timed, hierarchical_timer
from google.protobuf.internal.decoder import _DecodeVarint32 # type: ignore
from google.protobuf.internal.encoder import _EncodeVarint # type: ignore
INITIAL_POS = 33
SUPPORTED_DEMONSTRATION_VERSIONS = frozenset([0, 1])
@timed
def make_demo_buffer(
pair_infos: List[AgentInfoActionPairProto],
behavior_spec: BehaviorSpec,
sequence_length: int,
) -> AgentBuffer:
# Create and populate buffer using experiences
demo_raw_buffer = AgentBuffer()
demo_processed_buffer = AgentBuffer()
for idx, current_pair_info in enumerate(pair_infos):
if idx > len(pair_infos) - 2:
break
next_pair_info = pair_infos[idx + 1]
current_decision_step, current_terminal_step = steps_from_proto(
[current_pair_info.agent_info], behavior_spec
)
next_decision_step, next_terminal_step = steps_from_proto(
[next_pair_info.agent_info], behavior_spec
)
previous_action = (
np.array(pair_infos[idx].action_info.vector_actions, dtype=np.float32) * 0
)
if idx > 0:
previous_action = np.array(
pair_infos[idx - 1].action_info.vector_actions, dtype=np.float32
)
next_done = len(next_terminal_step) == 1
next_reward = 0
if len(next_terminal_step) == 1:
next_reward = next_terminal_step.reward[0]
else:
next_reward = next_decision_step.reward[0]
current_obs = None
if len(current_terminal_step) == 1:
current_obs = list(current_terminal_step.values())[0].obs
else:
current_obs = list(current_decision_step.values())[0].obs
demo_raw_buffer["done"].append(next_done)
demo_raw_buffer["rewards"].append(next_reward)
split_obs = SplitObservations.from_observations(current_obs)
for i, obs in enumerate(split_obs.visual_observations):
demo_raw_buffer["visual_obs%d" % i].append(obs)
demo_raw_buffer["vector_obs"].append(split_obs.vector_observations)
demo_raw_buffer["actions"].append(current_pair_info.action_info.vector_actions)
demo_raw_buffer["prev_action"].append(previous_action)
if next_done:
demo_raw_buffer.resequence_and_append(
demo_processed_buffer, batch_size=None, training_length=sequence_length
)
demo_raw_buffer.reset_agent()
demo_raw_buffer.resequence_and_append(
demo_processed_buffer, batch_size=None, training_length=sequence_length
)
return demo_processed_buffer
@timed
def demo_to_buffer(
file_path: str, sequence_length: int, expected_behavior_spec: BehaviorSpec = None
) -> Tuple[BehaviorSpec, AgentBuffer]:
"""
Loads demonstration file and uses it to fill training buffer.
:param file_path: Location of demonstration file (.demo).
:param sequence_length: Length of trajectories to fill buffer.
:return:
"""
behavior_spec, info_action_pair, _ = load_demonstration(file_path)
demo_buffer = make_demo_buffer(info_action_pair, behavior_spec, sequence_length)
if expected_behavior_spec:
# check action dimensions in demonstration match
if behavior_spec.action_spec != expected_behavior_spec.action_spec:
raise RuntimeError(
"The action spaces {} in demonstration do not match the policy's {}.".format(
behavior_spec.action_spec, expected_behavior_spec.action_spec
)
)
# check observations match
if len(behavior_spec.observation_shapes) != len(
expected_behavior_spec.observation_shapes
):
raise RuntimeError(
"The demonstrations do not have the same number of observations as the policy."
)
else:
for i, (demo_obs, policy_obs) in enumerate(
zip(
behavior_spec.observation_shapes,
expected_behavior_spec.observation_shapes,
)
):
if demo_obs != policy_obs:
raise RuntimeError(
f"The shape {demo_obs} for observation {i} in demonstration \
do not match the policy's {policy_obs}."
)
return behavior_spec, demo_buffer
def get_demo_files(path: str) -> List[str]:
"""
Retrieves the demonstration file(s) from a path.
:param path: Path of demonstration file or directory.
:return: List of demonstration files
Raises errors if |path| is invalid.
"""
if os.path.isfile(path):
if not path.endswith(".demo"):
raise ValueError("The path provided is not a '.demo' file.")
return [path]
elif os.path.isdir(path):
paths = [
os.path.join(path, name)
for name in os.listdir(path)
if name.endswith(".demo")
]
if not paths:
raise ValueError("There are no '.demo' files in the provided directory.")
return paths
else:
raise FileNotFoundError(
f"The demonstration file or directory {path} does not exist."
)
@timed
def load_demonstration(
file_path: str,
) -> Tuple[BehaviorSpec, List[AgentInfoActionPairProto], int]:
"""
Loads and parses a demonstration file.
:param file_path: Location of demonstration file (.demo).
:return: BrainParameter and list of AgentInfoActionPairProto containing demonstration data.
"""
# First 32 bytes of file dedicated to meta-data.
file_paths = get_demo_files(file_path)
behavior_spec = None
brain_param_proto = None
info_action_pairs = []
total_expected = 0
for _file_path in file_paths:
with open(_file_path, "rb") as fp:
with hierarchical_timer("read_file"):
data = fp.read()
next_pos, pos, obs_decoded = 0, 0, 0
while pos < len(data):
next_pos, pos = _DecodeVarint32(data, pos)
if obs_decoded == 0:
meta_data_proto = DemonstrationMetaProto()
meta_data_proto.ParseFromString(data[pos : pos + next_pos])
if (
meta_data_proto.api_version
not in SUPPORTED_DEMONSTRATION_VERSIONS
):
raise RuntimeError(
f"Can't load Demonstration data from an unsupported version ({meta_data_proto.api_version})"
)
total_expected += meta_data_proto.number_steps
pos = INITIAL_POS
if obs_decoded == 1:
brain_param_proto = BrainParametersProto()
brain_param_proto.ParseFromString(data[pos : pos + next_pos])
pos += next_pos
if obs_decoded > 1:
agent_info_action = AgentInfoActionPairProto()
agent_info_action.ParseFromString(data[pos : pos + next_pos])
if behavior_spec is None:
behavior_spec = behavior_spec_from_proto(
brain_param_proto, agent_info_action.agent_info
)
info_action_pairs.append(agent_info_action)
if len(info_action_pairs) == total_expected:
break
pos += next_pos
obs_decoded += 1
if not behavior_spec:
raise RuntimeError(
f"No BrainParameters found in demonstration file at {file_path}."
)
return behavior_spec, info_action_pairs, total_expected
def write_delimited(f, message):
msg_string = message.SerializeToString()
msg_size = len(msg_string)
_EncodeVarint(f.write, msg_size)
f.write(msg_string)
def write_demo(demo_path, meta_data_proto, brain_param_proto, agent_info_protos):
with open(demo_path, "wb") as f:
# write metadata
write_delimited(f, meta_data_proto)
f.seek(INITIAL_POS)
write_delimited(f, brain_param_proto)
for agent in agent_info_protos:
write_delimited(f, agent)