File size: 2,670 Bytes
406662d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | # Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This scripts run training with different RL libraries over a subset of the environments.
It calls the script ``scripts/reinforcement_learning/${args.lib_name}/train.py`` with the appropriate arguments.
Each training run has the corresponding "commit tag" appended to the run name, which allows comparing different
training logs of the same environments.
Example usage:
.. code-block:: bash
# for rsl-rl
python run_train_envs.py --lib-name rsl_rl
"""
import argparse
import subprocess
from test_settings import ISAACLAB_PATH, TEST_RL_ENVS
def parse_args() -> argparse.Namespace:
"""Parse the command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument(
"--lib-name",
type=str,
default="rsl_rl",
choices=["rsl_rl", "skrl", "rl_games", "sb3"],
help="The name of the library to use for training.",
)
return parser.parse_args()
def main(args: argparse.Namespace):
"""The main function."""
# get the git commit hash
git_commit_hash = subprocess.check_output(["git", "rev-parse", "HEAD"]).decode("utf-8").strip()
# add run name based on library
if args.lib_name == "rsl_rl":
extra_args = ["--run_name", git_commit_hash]
else:
# TODO: Modify this for other libraries as well to have commit tag in their saved run logs
extra_args = []
# train on each environment
for env_name in TEST_RL_ENVS:
# print a colored output to catch the attention of the user
# this should be a multi-line print statement
print("\033[91m==============================================\033[0m")
print("\033[91m==============================================\033[0m")
print(f"\033[91mTraining on {env_name} with {args.lib_name}...\033[0m")
print("\033[91m==============================================\033[0m")
print("\033[91m==============================================\033[0m")
# run the training script
subprocess.run(
[
f"{ISAACLAB_PATH}/isaaclab.sh",
"-p",
f"{ISAACLAB_PATH}/scripts/reinforcement_learning/{args.lib_name}/train.py",
"--task",
env_name,
"--headless",
]
+ extra_args,
check=False, # do not raise an error if the script fails
)
if __name__ == "__main__":
args_cli = parse_args()
main(args_cli)
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