251 lines
9.1 KiB
Python
251 lines
9.1 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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"""
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Script to play a checkpoint of an RL agent from skrl.
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Visit the skrl documentation (https://skrl.readthedocs.io) to see the examples structured in
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a more user-friendly way.
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"""
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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 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 skrl.")
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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",
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type=str,
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default=None,
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help=(
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"Name of the RL agent configuration entry point. Defaults to None, in which case the argument "
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"--algorithm is used to determine the default agent configuration entry point."
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),
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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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"--ml_framework",
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type=str,
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default="torch",
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choices=["torch", "jax", "jax-numpy"],
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help="The ML framework used for training the skrl agent.",
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)
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parser.add_argument(
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"--algorithm",
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type=str,
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default="PPO",
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choices=["AMP", "PPO", "IPPO", "MAPPO"],
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help="The RL algorithm used for training the skrl agent.",
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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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# 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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import skrl
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from packaging import version
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# check for minimum supported skrl version
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SKRL_VERSION = "1.4.3"
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if version.parse(skrl.__version__) < version.parse(SKRL_VERSION):
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skrl.logger.error(
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f"Unsupported skrl version: {skrl.__version__}. "
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f"Install supported version using 'pip install skrl>={SKRL_VERSION}'"
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)
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exit()
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if args_cli.ml_framework.startswith("torch"):
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from skrl.utils.runner.torch import Runner
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elif args_cli.ml_framework.startswith("jax"):
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from skrl.utils.runner.jax import Runner
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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.skrl import SkrlVecEnvWrapper
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import isaaclab_tasks # noqa: F401
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from isaaclab_tasks.utils import get_checkpoint_path
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from isaaclab_tasks.utils.hydra import hydra_task_config
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import mindbot.tasks # noqa: F401
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# config shortcuts
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if args_cli.agent is None:
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algorithm = args_cli.algorithm.lower()
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agent_cfg_entry_point = "skrl_cfg_entry_point" if algorithm in ["ppo"] else f"skrl_{algorithm}_cfg_entry_point"
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else:
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agent_cfg_entry_point = args_cli.agent
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algorithm = agent_cfg_entry_point.split("_cfg")[0].split("skrl_")[-1].lower()
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@hydra_task_config(args_cli.task, agent_cfg_entry_point)
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def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, experiment_cfg: dict):
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"""Play with skrl 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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# 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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env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
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# configure the ML framework into the global skrl variable
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if args_cli.ml_framework.startswith("jax"):
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skrl.config.jax.backend = "jax" if args_cli.ml_framework == "jax" else "numpy"
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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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# set the agent and environment seed from command line
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# note: certain randomization occur in the environment initialization so we set the seed here
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experiment_cfg["seed"] = args_cli.seed if args_cli.seed is not None else experiment_cfg["seed"]
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env_cfg.seed = experiment_cfg["seed"]
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# specify directory for logging experiments (load checkpoint)
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log_root_path = os.path.join("logs", "skrl", experiment_cfg["agent"]["experiment"]["directory"])
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log_root_path = os.path.abspath(log_root_path)
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print(f"[INFO] Loading experiment from directory: {log_root_path}")
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# get checkpoint path
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if args_cli.use_pretrained_checkpoint:
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resume_path = get_published_pretrained_checkpoint("skrl", train_task_name)
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if not resume_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:
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resume_path = os.path.abspath(args_cli.checkpoint)
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else:
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resume_path = get_checkpoint_path(
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log_root_path, run_dir=f".*_{algorithm}_{args_cli.ml_framework}", other_dirs=["checkpoints"]
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)
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log_dir = os.path.dirname(os.path.dirname(resume_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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# convert to single-agent instance if required by the RL algorithm
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if isinstance(env.unwrapped, DirectMARLEnv) and algorithm in ["ppo"]:
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env = multi_agent_to_single_agent(env)
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# get environment (step) dt for real-time evaluation
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try:
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dt = env.step_dt
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except AttributeError:
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dt = env.unwrapped.step_dt
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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 skrl
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env = SkrlVecEnvWrapper(env, ml_framework=args_cli.ml_framework) # same as: `wrap_env(env, wrapper="auto")`
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# configure and instantiate the skrl runner
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# https://skrl.readthedocs.io/en/latest/api/utils/runner.html
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experiment_cfg["trainer"]["close_environment_at_exit"] = False
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experiment_cfg["agent"]["experiment"]["write_interval"] = 0 # don't log to TensorBoard
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experiment_cfg["agent"]["experiment"]["checkpoint_interval"] = 0 # don't generate checkpoints
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runner = Runner(env, experiment_cfg)
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print(f"[INFO] Loading model checkpoint from: {resume_path}")
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runner.agent.load(resume_path)
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# set agent to evaluation mode
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runner.agent.set_running_mode("eval")
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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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outputs = runner.agent.act(obs, timestep=0, timesteps=0)
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# - multi-agent (deterministic) actions
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if hasattr(env, "possible_agents"):
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actions = {a: outputs[-1][a].get("mean_actions", outputs[0][a]) for a in env.possible_agents}
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# - single-agent (deterministic) actions
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else:
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actions = outputs[-1].get("mean_actions", outputs[0])
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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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