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"""Script to train RL agent with RSL-RL."""
"""Launch Isaac Sim Simulator first."""
import argparse
import sys
from isaaclab.app import AppLauncher
# local imports
import cli_args # isort: skip
# add argparse arguments
parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.")
parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.")
parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).")
parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).")
parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
parser.add_argument("--task", type=str, default=None, help="Name of the task.")
parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.")
parser.add_argument(
"--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes."
)
# append RSL-RL cli arguments
cli_args.add_rsl_rl_args(parser)
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
args_cli, hydra_args = parser.parse_known_args()
# always enable cameras to record video
if args_cli.video:
args_cli.enable_cameras = True
# clear out sys.argv for Hydra
sys.argv = [sys.argv[0]] + hydra_args
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
"""Check for minimum supported RSL-RL version."""
import importlib.metadata as metadata
import platform
from packaging import version
# for distributed training, check minimum supported rsl-rl version
RSL_RL_VERSION = "2.3.1"
installed_version = metadata.version("rsl-rl-lib")
if args_cli.distributed and version.parse(installed_version) < version.parse(RSL_RL_VERSION):
if platform.system() == "Windows":
cmd = [r".\isaaclab.bat", "-p", "-m", "pip", "install", f"rsl-rl-lib=={RSL_RL_VERSION}"]
else:
cmd = ["./isaaclab.sh", "-p", "-m", "pip", "install", f"rsl-rl-lib=={RSL_RL_VERSION}"]
print(
f"Please install the correct version of RSL-RL.\nExisting version is: '{installed_version}'"
f" and required version is: '{RSL_RL_VERSION}'.\nTo install the correct version, run:"
f"\n\n\t{' '.join(cmd)}\n"
)
exit(1)
"""Rest everything follows."""
import os
from datetime import datetime
import gymnasium as gym
import isaaclab_tasks # noqa: F401
import pair_lab.tasks # noqa: F401
import torch
from isaaclab.envs import (
DirectMARLEnv,
DirectMARLEnvCfg,
DirectRLEnvCfg,
ManagerBasedRLEnvCfg,
multi_agent_to_single_agent,
)
from isaaclab.utils.dict import print_dict
from isaaclab.utils.io import dump_pickle, dump_yaml
from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlVecEnvWrapper
from isaaclab_tasks.utils import get_checkpoint_path
from isaaclab_tasks.utils.hydra import hydra_task_config
from rsl_rl.runners import OnPolicyRunner
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = False
@hydra_task_config(args_cli.task, "rsl_rl_cfg_entry_point")
def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: RslRlOnPolicyRunnerCfg):
"""Train with RSL-RL agent."""
# override configurations with non-hydra CLI arguments
agent_cfg = cli_args.update_rsl_rl_cfg(agent_cfg, args_cli)
env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
agent_cfg.max_iterations = (
args_cli.max_iterations if args_cli.max_iterations is not None else agent_cfg.max_iterations
)
# set the environment seed
# note: certain randomizations occur in the environment initialization so we set the seed here
env_cfg.seed = agent_cfg.seed
env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
# multi-gpu training configuration
if args_cli.distributed:
env_cfg.sim.device = f"cuda:{app_launcher.local_rank}"
agent_cfg.device = f"cuda:{app_launcher.local_rank}"
# set seed to have diversity in different threads
seed = agent_cfg.seed + app_launcher.local_rank
env_cfg.seed = seed
agent_cfg.seed = seed
# specify directory for logging experiments
log_root_path = os.path.join("logs", "rsl_rl", agent_cfg.experiment_name)
log_root_path = os.path.abspath(log_root_path)
print(f"[INFO] Logging experiment in directory: {log_root_path}")
# specify directory for logging runs: {time-stamp}_{run_name}
log_dir = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
# The Ray Tune workflow extracts experiment name using the logging line below, hence, do not change it (see PR #2346, comment-2819298849)
print(f"Exact experiment name requested from command line: {log_dir}")
if agent_cfg.run_name:
log_dir += f"_{agent_cfg.run_name}"
log_dir = os.path.join(log_root_path, log_dir)
# create isaac environment
env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
# convert to single-agent instance if required by the RL algorithm
if isinstance(env.unwrapped, DirectMARLEnv):
env = multi_agent_to_single_agent(env)
# save resume path before creating a new log_dir
if agent_cfg.resume or agent_cfg.algorithm.class_name == "Distillation":
resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
# wrap for video recording
if args_cli.video:
video_kwargs = {
"video_folder": os.path.join(log_dir, "videos", "train"),
"step_trigger": lambda step: step % args_cli.video_interval == 0,
"video_length": args_cli.video_length,
"disable_logger": True,
}
print("[INFO] Recording videos during training.")
print_dict(video_kwargs, nesting=4)
env = gym.wrappers.RecordVideo(env, **video_kwargs)
# wrap around environment for rsl-rl
env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions)
# create runner from rsl-rl
runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device)
# write git state to logs
runner.add_git_repo_to_log(__file__)
# load the checkpoint
if agent_cfg.resume or agent_cfg.algorithm.class_name == "Distillation":
print(f"[INFO]: Loading model checkpoint from: {resume_path}")
# load previously trained model
runner.load(resume_path)
# dump the configuration into log-directory
dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg)
dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg)
dump_pickle(os.path.join(log_dir, "params", "env.pkl"), env_cfg)
dump_pickle(os.path.join(log_dir, "params", "agent.pkl"), agent_cfg)
# run training
runner.learn(num_learning_iterations=agent_cfg.max_iterations, init_at_random_ep_len=True)
# close the simulator
env.close()
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
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