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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179 行
6.1 KiB

import argparse
import os
import shutil
import sys
import subprocess
import time
from typing import Any
from .yamato_utils import (
find_executables,
get_base_path,
get_base_output_path,
run_standalone_build,
init_venv,
override_config_file,
override_legacy_config_file,
checkout_csharp_version,
undo_git_checkout,
)
def run_training(python_version: str, csharp_version: str) -> bool:
latest = "latest"
run_id = int(time.time() * 1000.0)
print(
f"Running training with python={python_version or latest} and c#={csharp_version or latest}"
)
output_dir = "models" if python_version else "results"
nn_file_expected = f"./{output_dir}/{run_id}/3DBall.nn"
onnx_file_expected = f"./{output_dir}/{run_id}/3DBall.onnx"
frozen_graph_file_expected = f"./{output_dir}/{run_id}/3DBall/frozen_graph_def.pb"
if os.path.exists(nn_file_expected):
# Should never happen - make sure nothing leftover from an old test.
print("Artifacts from previous build found!")
return False
base_path = get_base_path()
print(f"Running in base path {base_path}")
# Only build the standalone player if we're overriding the C# version
# Otherwise we'll use the one built earlier in the pipeline.
if csharp_version is not None:
# We can't rely on the old C# code recognizing the commandline argument to set the output
# So rename testPlayer (containing the most recent build) to something else temporarily
artifact_path = get_base_output_path()
full_player_path = os.path.join(artifact_path, "testPlayer.app")
temp_player_path = os.path.join(artifact_path, "temp_testPlayer.app")
final_player_path = os.path.join(
artifact_path, f"testPlayer_{csharp_version}.app"
)
os.rename(full_player_path, temp_player_path)
checkout_csharp_version(csharp_version)
build_returncode = run_standalone_build(base_path)
if build_returncode != 0:
print(f"Standalone build FAILED! with return code {build_returncode}")
return False
# Now rename the newly-built executable, and restore the old one
os.rename(full_player_path, final_player_path)
os.rename(temp_player_path, full_player_path)
standalone_player_path = f"testPlayer_{csharp_version}"
else:
standalone_player_path = "testPlayer"
venv_path = init_venv(python_version)
# Copy the default training config but override the max_steps parameter,
# and reduce the batch_size and buffer_size enough to ensure an update step happens.
yaml_out = "override.yaml"
if python_version:
overrides: Any = {"max_steps": 100, "batch_size": 10, "buffer_size": 10}
override_legacy_config_file(
python_version, "config/trainer_config.yaml", yaml_out, **overrides
)
else:
overrides = {
"hyperparameters": {"batch_size": 10, "buffer_size": 10},
"max_steps": 100,
}
override_config_file("config/ppo/3DBall.yaml", yaml_out, overrides)
env_path = os.path.join(get_base_output_path(), standalone_player_path + ".app")
mla_learn_cmd = (
f"mlagents-learn {yaml_out} --force --env={env_path} "
f"--run-id={run_id} --no-graphics --env-args -logFile -"
) # noqa
res = subprocess.run(
f"source {venv_path}/bin/activate; {mla_learn_cmd}", shell=True
)
# Save models as artifacts (only if we're using latest python and C#)
if csharp_version is None and python_version is None:
model_artifacts_dir = os.path.join(get_base_output_path(), "models")
os.makedirs(model_artifacts_dir, exist_ok=True)
shutil.copy(nn_file_expected, model_artifacts_dir)
shutil.copy(onnx_file_expected, model_artifacts_dir)
shutil.copy(frozen_graph_file_expected, model_artifacts_dir)
if (
res.returncode != 0
or not os.path.exists(nn_file_expected)
or not os.path.exists(onnx_file_expected)
):
print("mlagents-learn run FAILED!")
return False
if csharp_version is None and python_version is None:
# Use abs path so that loading doesn't get confused
model_path = os.path.abspath(os.path.dirname(nn_file_expected))
# Onnx loading for overrides not currently supported, but this is
# where to add it in when it is.
for extension in ["nn"]:
inference_ok = run_inference(env_path, model_path, extension)
if not inference_ok:
return False
print("mlagents-learn run SUCCEEDED!")
return True
def run_inference(env_path: str, output_path: str, model_extension: str) -> bool:
start_time = time.time()
exes = find_executables(env_path)
if len(exes) != 1:
print(f"Can't determine the player executable in {env_path}. Found {exes}.")
return False
log_output_path = f"{get_base_output_path()}/inference.{model_extension}.txt"
exe_path = exes[0]
args = [
exe_path,
"-nographics",
"-batchmode",
"-logfile",
log_output_path,
"--mlagents-override-model-directory",
output_path,
"--mlagents-quit-on-load-failure",
"--mlagents-quit-after-episodes",
"1",
"--mlagents-override-model-extension",
model_extension,
]
res = subprocess.run(args)
end_time = time.time()
if res.returncode != 0:
print("Error running inference!")
print("Command line: " + " ".join(args))
subprocess.run(["cat", log_output_path])
return False
else:
print(f"Inference succeeded! Took {end_time - start_time} seconds")
return True
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--python", default=None)
parser.add_argument("--csharp", default=None)
args = parser.parse_args()
try:
ok = run_training(args.python, args.csharp)
if not ok:
sys.exit(1)
finally:
# Cleanup - this gets executed even if we hit sys.exit()
undo_git_checkout()
if __name__ == "__main__":
main()