214 lines
7.8 KiB
Python
214 lines
7.8 KiB
Python
# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
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# All rights reserved.
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#
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# SPDX-License-Identifier: BSD-3-Clause
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"""Script to play a checkpoint if an RL agent from Stable-Baselines3."""
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"""Launch Isaac Sim Simulator first."""
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import argparse
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import sys
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from pathlib import Path
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from isaaclab.app import AppLauncher
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# add argparse arguments
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parser = argparse.ArgumentParser(description="Play a checkpoint of an RL agent from Stable-Baselines3.")
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parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.")
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parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).")
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parser.add_argument(
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"--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
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)
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parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
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parser.add_argument("--task", type=str, default=None, help="Name of the task.")
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parser.add_argument(
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"--agent", type=str, default="sb3_cfg_entry_point", help="Name of the RL agent configuration entry point."
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)
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parser.add_argument("--checkpoint", type=str, default=None, help="Path to model checkpoint.")
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parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
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parser.add_argument(
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"--use_pretrained_checkpoint",
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action="store_true",
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help="Use the pre-trained checkpoint from Nucleus.",
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)
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parser.add_argument(
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"--use_last_checkpoint",
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action="store_true",
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help="When no checkpoint provided, use the last saved model. Otherwise use the best saved model.",
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)
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parser.add_argument("--real-time", action="store_true", default=False, help="Run in real-time, if possible.")
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parser.add_argument(
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"--keep_all_info",
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action="store_true",
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default=False,
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help="Use a slower SB3 wrapper but keep all the extra training info.",
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)
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# append AppLauncher cli args
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AppLauncher.add_app_launcher_args(parser)
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# parse the arguments
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args_cli, hydra_args = parser.parse_known_args()
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# always enable cameras to record video
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if args_cli.video:
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args_cli.enable_cameras = True
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# clear out sys.argv for Hydra
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sys.argv = [sys.argv[0]] + hydra_args
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# launch omniverse app
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app_launcher = AppLauncher(args_cli)
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simulation_app = app_launcher.app
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"""Rest everything follows."""
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import gymnasium as gym
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import os
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import random
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import time
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import torch
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from stable_baselines3 import PPO
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from stable_baselines3.common.vec_env import VecNormalize
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from isaaclab.envs import (
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DirectMARLEnv,
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DirectMARLEnvCfg,
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DirectRLEnvCfg,
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ManagerBasedRLEnvCfg,
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multi_agent_to_single_agent,
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)
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from isaaclab.utils.dict import print_dict
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from isaaclab.utils.pretrained_checkpoint import get_published_pretrained_checkpoint
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from isaaclab_rl.sb3 import Sb3VecEnvWrapper, process_sb3_cfg
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import isaaclab_tasks # noqa: F401
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from isaaclab_tasks.utils.hydra import hydra_task_config
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from isaaclab_tasks.utils.parse_cfg import get_checkpoint_path
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import mindbot.tasks # noqa: F401
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@hydra_task_config(args_cli.task, args_cli.agent)
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def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict):
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"""Play with stable-baselines agent."""
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# grab task name for checkpoint path
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task_name = args_cli.task.split(":")[-1]
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train_task_name = task_name.replace("-Play", "")
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# randomly sample a seed if seed = -1
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if args_cli.seed == -1:
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args_cli.seed = random.randint(0, 10000)
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# override configurations with non-hydra CLI arguments
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env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
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agent_cfg["seed"] = args_cli.seed if args_cli.seed is not None else agent_cfg["seed"]
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# set the environment seed
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# note: certain randomizations occur in the environment initialization so we set the seed here
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env_cfg.seed = agent_cfg["seed"]
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env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
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# directory for logging into
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log_root_path = os.path.join("logs", "sb3", train_task_name)
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log_root_path = os.path.abspath(log_root_path)
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# checkpoint and log_dir stuff
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if args_cli.use_pretrained_checkpoint:
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checkpoint_path = get_published_pretrained_checkpoint("sb3", train_task_name)
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if not checkpoint_path:
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print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.")
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return
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elif args_cli.checkpoint is None:
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# FIXME: last checkpoint doesn't seem to really use the last one'
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if args_cli.use_last_checkpoint:
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checkpoint = "model_.*.zip"
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else:
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checkpoint = "model.zip"
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checkpoint_path = get_checkpoint_path(log_root_path, ".*", checkpoint, sort_alpha=False)
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else:
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checkpoint_path = args_cli.checkpoint
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log_dir = os.path.dirname(checkpoint_path)
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# set the log directory for the environment (works for all environment types)
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env_cfg.log_dir = log_dir
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# create isaac environment
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env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
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# post-process agent configuration
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agent_cfg = process_sb3_cfg(agent_cfg, env.unwrapped.num_envs)
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# convert to single-agent instance if required by the RL algorithm
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if isinstance(env.unwrapped, DirectMARLEnv):
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env = multi_agent_to_single_agent(env)
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# wrap for video recording
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if args_cli.video:
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video_kwargs = {
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"video_folder": os.path.join(log_dir, "videos", "play"),
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"step_trigger": lambda step: step == 0,
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"video_length": args_cli.video_length,
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"disable_logger": True,
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}
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print("[INFO] Recording videos during training.")
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print_dict(video_kwargs, nesting=4)
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env = gym.wrappers.RecordVideo(env, **video_kwargs)
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# wrap around environment for stable baselines
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env = Sb3VecEnvWrapper(env, fast_variant=not args_cli.keep_all_info)
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vec_norm_path = checkpoint_path.replace("/model", "/model_vecnormalize").replace(".zip", ".pkl")
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vec_norm_path = Path(vec_norm_path)
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# normalize environment (if needed)
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if vec_norm_path.exists():
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print(f"Loading saved normalization: {vec_norm_path}")
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env = VecNormalize.load(vec_norm_path, env)
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# do not update them at test time
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env.training = False
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# reward normalization is not needed at test time
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env.norm_reward = False
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elif "normalize_input" in agent_cfg:
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env = VecNormalize(
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env,
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training=True,
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norm_obs="normalize_input" in agent_cfg and agent_cfg.pop("normalize_input"),
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clip_obs="clip_obs" in agent_cfg and agent_cfg.pop("clip_obs"),
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)
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# create agent from stable baselines
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print(f"Loading checkpoint from: {checkpoint_path}")
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agent = PPO.load(checkpoint_path, env, print_system_info=True)
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dt = env.unwrapped.step_dt
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# reset environment
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obs = env.reset()
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timestep = 0
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# simulate environment
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while simulation_app.is_running():
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start_time = time.time()
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# run everything in inference mode
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with torch.inference_mode():
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# agent stepping
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actions, _ = agent.predict(obs, deterministic=True)
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# env stepping
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obs, _, _, _ = env.step(actions)
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if args_cli.video:
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timestep += 1
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# Exit the play loop after recording one video
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if timestep == args_cli.video_length:
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break
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# time delay for real-time evaluation
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sleep_time = dt - (time.time() - start_time)
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if args_cli.real_time and sleep_time > 0:
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time.sleep(sleep_time)
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# close the simulator
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env.close()
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if __name__ == "__main__":
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# run the main function
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main()
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# close sim app
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simulation_app.close()
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