Initial commit
This commit is contained in:
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# Copyright (c) 2022-2026, 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 print all the available environments in Isaac Lab.
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The script iterates over all registered environments and stores the details in a table.
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It prints the name of the environment, the entry point and the config file.
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All the environments are registered in the `dex_workbench` extension. They start
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with `Isaac` in their name.
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"""
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"""Launch Isaac Sim Simulator first."""
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import argparse
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import contextlib
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from isaaclab.app import AppLauncher
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# add argparse arguments
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parser = argparse.ArgumentParser(description="List Isaac Lab environments.")
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parser.add_argument("--keyword", type=str, default=None, help="Keyword to filter environments.")
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parser.add_argument(
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"--show_presets",
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action="store_true",
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default=False,
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help=(
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"Show available preset selectors for each environment. "
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"Presets are grouped by selector type: physics (physics=NAME), "
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"renderer (renderer=NAME), and domain (presets=NAME)."
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),
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)
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# parse the arguments
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args_cli = parser.parse_args()
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# launch omniverse app
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app_launcher = AppLauncher(headless=True)
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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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from prettytable import PrettyTable
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import dex_workbench.tasks # noqa: F401
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# PLACEHOLDER: Extension template (do not remove this comment)
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with contextlib.suppress(ImportError):
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import dex_workbench.tasks_experimental # noqa: F401
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def _format_presets(preset_map: dict | None) -> str:
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"""Format a preset map returned by :func:`enumerate_task_presets` into a human-readable string.
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Args:
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preset_map: Mapping of :class:`~dex_workbench.utils.preset_target.PresetTarget`
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to sorted preset name lists, or ``None`` when the env cfg could not be loaded.
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Returns:
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A multi-line string with one line per non-empty selector category, or a
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short placeholder when no presets are available or the cfg failed to load.
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"""
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if preset_map is None:
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return "(unavailable)"
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from dex_workbench.utils.preset_target import PresetTarget
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lines = []
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labels = {
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PresetTarget.PHYSICS: "physics",
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PresetTarget.RENDERER: "renderer",
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PresetTarget.DOMAIN: "domain",
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}
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for target, label in labels.items():
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names = preset_map.get(target, [])
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if names:
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lines.append(f"{label}: {', '.join(names)}")
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return "\n".join(lines) if lines else "(none)"
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def main():
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"""Print all environments registered in `dex_workbench` extension."""
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# Collect matching task specs first so we can enumerate presets in one pass.
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task_specs = [
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spec
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for spec in gym.registry.values()
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if "Template-" in spec.id and (args_cli.keyword is None or args_cli.keyword in spec.id)
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]
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if args_cli.show_presets:
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from dex_workbench.utils.preset_cli import enumerate_task_presets
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table = PrettyTable(["S. No.", "Task Name", "Entry Point", "Config", "Presets"])
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table.title = "Available Environments in Isaac Lab"
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table.align["Task Name"] = "l"
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table.align["Entry Point"] = "l"
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table.align["Config"] = "l"
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table.align["Presets"] = "l"
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for index, spec in enumerate(task_specs):
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preset_map = enumerate_task_presets(spec.id)
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table.add_row(
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[
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index + 1,
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spec.id,
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spec.entry_point,
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spec.kwargs["env_cfg_entry_point"],
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_format_presets(preset_map),
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]
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)
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else:
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table = PrettyTable(["S. No.", "Task Name", "Entry Point", "Config"])
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table.title = "Available Environments in Isaac Lab"
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table.align["Task Name"] = "l"
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table.align["Entry Point"] = "l"
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table.align["Config"] = "l"
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for index, spec in enumerate(task_specs):
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table.add_row([index + 1, spec.id, spec.entry_point, spec.kwargs["env_cfg_entry_point"]])
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print(table)
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if __name__ == "__main__":
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try:
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# run the main function
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main()
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except Exception as e:
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raise e
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finally:
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# close the app
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simulation_app.close()
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@@ -0,0 +1,86 @@
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# Copyright (c) 2022-2026, 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 an environment with random action agent."""
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import argparse
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import contextlib
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import sys
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import gymnasium as gym
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import torch
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import isaaclab_tasks # noqa: F401
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with contextlib.suppress(ImportError):
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import isaaclab_tasks_experimental # noqa: F401
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from isaaclab_tasks.utils import (
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add_launcher_args,
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launch_simulation,
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resolve_task_config,
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setup_preset_cli,
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)
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# add argparse arguments
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parser = argparse.ArgumentParser(description="Random agent for Isaac Lab environments.")
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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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# append AppLauncher cli args
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add_launcher_args(parser)
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# simple agents should open Kit visualizer by default
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parser.set_defaults(visualizer=["kit"])
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args_cli, hydra_args = setup_preset_cli(parser)
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sys.argv = [sys.argv[0]] + hydra_args
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import dex_workbench.tasks # noqa: F401
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def main():
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"""Random actions agent with Isaac Lab environment."""
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torch.manual_seed(42)
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# parse configuration via Hydra (supports preset selection, e.g. env.sim.physics=newton_mjwarp)
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env_cfg, _ = resolve_task_config(args_cli.task, "")
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with launch_simulation(env_cfg, args_cli):
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# override with 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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if args_cli.disable_fabric:
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env_cfg.sim.use_fabric = False
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# create environment
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env = gym.make(args_cli.task, cfg=env_cfg)
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# print info (this is vectorized environment)
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print(f"[INFO]: Gym observation space: {env.observation_space}")
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print(f"[INFO]: Gym action space: {env.action_space}")
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# reset environment
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env.reset()
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# simulate environment
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sim = env.unwrapped.sim
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while True:
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if sim.visualizers:
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# visualizer mode: run until the visualizer window is closed
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if not any(v.is_running() and not v.is_closed for v in sim.visualizers):
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break
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# run everything in inference mode
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with torch.inference_mode():
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# sample actions from -1 to 1
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actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1
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# apply actions
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env.step(actions)
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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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# Copyright (c) 2022-2026, 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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from __future__ import annotations
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import argparse
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import random
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from isaaclab_rl.rsl_rl import RslRlBaseRunnerCfg
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def add_rsl_rl_args(parser: argparse.ArgumentParser):
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"""Add RSL-RL arguments to the parser.
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Args:
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parser: The parser to add the arguments to.
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"""
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# create a new argument group
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arg_group = parser.add_argument_group("rsl_rl", description="Arguments for RSL-RL agent.")
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# -- experiment arguments
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arg_group.add_argument(
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"--experiment_name", type=str, default=None, help="Name of the experiment folder where logs will be stored."
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)
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arg_group.add_argument("--run_name", type=str, default=None, help="Run name suffix to the log directory.")
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# -- load arguments
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arg_group.add_argument("--resume", action="store_true", default=False, help="Whether to resume from a checkpoint.")
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arg_group.add_argument("--load_run", type=str, default=None, help="Name of the run folder to resume from.")
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arg_group.add_argument("--checkpoint", type=str, default=None, help="Checkpoint file to resume from.")
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# -- logger arguments
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arg_group.add_argument(
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"--logger", type=str, default=None, choices={"wandb", "tensorboard", "neptune"}, help="Logger module to use."
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)
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arg_group.add_argument(
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"--log_project_name", type=str, default=None, help="Name of the logging project when using wandb or neptune."
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)
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def parse_rsl_rl_cfg(task_name: str, args_cli: argparse.Namespace) -> RslRlBaseRunnerCfg:
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"""Parse configuration for RSL-RL agent based on inputs.
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Args:
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task_name: The name of the environment.
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args_cli: The command line arguments.
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Returns:
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The parsed configuration for RSL-RL agent based on inputs.
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"""
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from isaaclab_tasks.utils.parse_cfg import load_cfg_from_registry
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# load the default configuration
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rslrl_cfg: RslRlBaseRunnerCfg = load_cfg_from_registry(task_name, "rsl_rl_cfg_entry_point")
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rslrl_cfg = update_rsl_rl_cfg(rslrl_cfg, args_cli)
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return rslrl_cfg
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def update_rsl_rl_cfg(agent_cfg: RslRlBaseRunnerCfg, args_cli: argparse.Namespace):
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"""Update configuration for RSL-RL agent based on inputs.
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Args:
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agent_cfg: The configuration for RSL-RL agent.
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args_cli: The command line arguments.
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Returns:
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The updated configuration for RSL-RL agent based on inputs.
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"""
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# override the default configuration with CLI arguments
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if hasattr(args_cli, "seed") and args_cli.seed is not None:
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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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agent_cfg.seed = args_cli.seed
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if args_cli.resume is not None:
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agent_cfg.resume = args_cli.resume
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if args_cli.load_run is not None:
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agent_cfg.load_run = args_cli.load_run
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if args_cli.checkpoint is not None:
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agent_cfg.load_checkpoint = args_cli.checkpoint
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if args_cli.experiment_name is not None:
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agent_cfg.experiment_name = args_cli.experiment_name
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if args_cli.run_name is not None:
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agent_cfg.run_name = args_cli.run_name
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if args_cli.logger is not None:
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agent_cfg.logger = args_cli.logger
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# set the project name for wandb and neptune
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if agent_cfg.logger in {"wandb", "neptune"} and args_cli.log_project_name:
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agent_cfg.wandb_project = args_cli.log_project_name
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agent_cfg.neptune_project = args_cli.log_project_name
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return agent_cfg
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@@ -0,0 +1,251 @@
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# Copyright (c) 2022-2026, 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 RSL-RL."""
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import warnings
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warnings.warn(
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"scripts/reinforcement_learning/rsl_rl/play.py is deprecated. Use "
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"`./isaaclab.sh play --rl_library rsl_rl --task <TASK>` instead. "
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"Example: `./isaaclab.sh play --rl_library rsl_rl --task Isaac-Cartpole-v0`.",
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DeprecationWarning,
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stacklevel=1,
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)
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import argparse
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import contextlib
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import importlib.metadata as metadata
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import os
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import sys
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import time
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import gymnasium as gym
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import torch
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from packaging import version
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from rsl_rl.runners import DistillationRunner, OnPolicyRunner
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from isaaclab.envs import DirectMARLEnvCfg, DirectRLEnvCfg, ManagerBasedRLEnvCfg
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from isaaclab.utils.assets import retrieve_file_path
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from isaaclab.utils.dict import print_dict
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from isaaclab.utils.seed import configure_seed
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from isaaclab.utils.string import list_intersection, string_to_callable
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from isaaclab_rl.rsl_rl import (
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RslRlBaseRunnerCfg,
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RslRlVecEnvWrapper,
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export_policy_as_jit,
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export_policy_as_onnx,
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handle_deprecated_rsl_rl_cfg,
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)
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from isaaclab_rl.utils.pretrained_checkpoint import get_published_pretrained_checkpoint
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import isaaclab_tasks # noqa: F401
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from isaaclab_tasks.utils import (
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add_launcher_args,
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get_checkpoint_path,
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launch_simulation,
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setup_preset_cli,
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)
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from isaaclab_tasks.utils.hydra import hydra_task_config
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# local imports
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import cli_args # isort: skip
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import dex_workbench.tasks # noqa: F401
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with contextlib.suppress(ImportError):
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import isaaclab_tasks_experimental # noqa: F401
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# -- argparse ----------------------------------------------------------------
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parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.")
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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="rsl_rl_cfg_entry_point", help="Name of the RL agent configuration entry point."
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)
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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("--real-time", action="store_true", default=False, help="Run in real-time, if possible.")
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parser.add_argument("--external_callback", default=None, help="Fully qualified path to an externally defined callback.")
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cli_args.add_rsl_rl_args(parser)
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add_launcher_args(parser)
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args_cli, remaining_args = setup_preset_cli(parser)
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if args_cli.video:
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args_cli.enable_cameras = True
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# Call an external callback if requested. This gives opportunity to external code to register the environments
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# The function is expected to return a list of arguments that were not consumed by the callback.
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remaining_args_env_registration = None
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if args_cli.external_callback:
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external_callback_function = string_to_callable(args_cli.external_callback, separator=".")
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remaining_args_env_registration = external_callback_function()
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# clear out sys.argv for Hydra
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# The remaining arguments are the arguments that were not consumed by both this scripts
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# argparser and (optionally) the external callback function. Both sides of this
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# intersection are pre-fold (the callback reads the user's original sys.argv), so
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# preset tokens like ``physics=NAME`` compare correctly here. Fold runs after.
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remaining_args = list_intersection(remaining_args, remaining_args_env_registration)
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sys.argv = [sys.argv[0]] + remaining_args
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# Check for installed RSL-RL version
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installed_version = metadata.version("rsl-rl-lib")
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||||
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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: RslRlBaseRunnerCfg):
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"""Play with RSL-RL agent."""
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with launch_simulation(env_cfg, args_cli):
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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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agent_cfg = cli_args.update_rsl_rl_cfg(agent_cfg, args_cli)
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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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||||
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# handle deprecated configurations
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||||
agent_cfg = handle_deprecated_rsl_rl_cfg(agent_cfg, installed_version)
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||||
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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
|
||||
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
|
||||
|
||||
# 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] Loading experiment from directory: {log_root_path}")
|
||||
if args_cli.use_pretrained_checkpoint:
|
||||
resume_path = get_published_pretrained_checkpoint("rsl_rl", train_task_name)
|
||||
if not resume_path:
|
||||
print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.")
|
||||
return
|
||||
elif args_cli.checkpoint:
|
||||
resume_path = retrieve_file_path(args_cli.checkpoint)
|
||||
else:
|
||||
resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
|
||||
|
||||
log_dir = os.path.dirname(resume_path)
|
||||
|
||||
# set the log directory for the environment
|
||||
env_cfg.log_dir = 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.cfg, DirectMARLEnvCfg):
|
||||
from isaaclab.envs import multi_agent_to_single_agent
|
||||
|
||||
env = multi_agent_to_single_agent(env)
|
||||
|
||||
# wrap for video recording
|
||||
if args_cli.video:
|
||||
video_kwargs = {
|
||||
"video_folder": os.path.join(log_dir, "videos", "play"),
|
||||
"step_trigger": lambda step: step == 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)
|
||||
|
||||
print(f"[INFO]: Loading model checkpoint from: {resume_path}")
|
||||
# load previously trained model
|
||||
if agent_cfg.class_name == "OnPolicyRunner":
|
||||
runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=None, device=agent_cfg.device)
|
||||
elif agent_cfg.class_name == "DistillationRunner":
|
||||
runner = DistillationRunner(env, agent_cfg.to_dict(), log_dir=None, device=agent_cfg.device)
|
||||
else:
|
||||
raise ValueError(f"Unsupported runner class: {agent_cfg.class_name}")
|
||||
# configure_seed must be called after runner construction so that PyTorch deterministic settings
|
||||
# do not interfere with the runner's internal initialization.
|
||||
if args_cli.deterministic:
|
||||
configure_seed(env_cfg.seed, True)
|
||||
runner.load(resume_path)
|
||||
|
||||
# obtain the trained policy for inference
|
||||
policy = runner.get_inference_policy(device=env.unwrapped.device)
|
||||
|
||||
# export the trained policy to JIT and ONNX formats
|
||||
export_model_dir = os.path.join(os.path.dirname(resume_path), "exported")
|
||||
|
||||
if version.parse(installed_version) >= version.parse("4.0.0"):
|
||||
# use the new export functions for rsl-rl >= 4.0.0
|
||||
runner.export_policy_to_jit(path=export_model_dir, filename="policy.pt")
|
||||
runner.export_policy_to_onnx(path=export_model_dir, filename="policy.onnx")
|
||||
policy_nn = None # Not needed for rsl-rl >= 4.0.0
|
||||
else:
|
||||
# extract the neural network for rsl-rl < 4.0.0
|
||||
if version.parse(installed_version) >= version.parse("2.3.0"):
|
||||
policy_nn = runner.alg.policy
|
||||
else:
|
||||
policy_nn = runner.alg.actor_critic
|
||||
|
||||
# extract the normalizer
|
||||
if hasattr(policy_nn, "actor_obs_normalizer"):
|
||||
normalizer = policy_nn.actor_obs_normalizer
|
||||
elif hasattr(policy_nn, "student_obs_normalizer"):
|
||||
normalizer = policy_nn.student_obs_normalizer
|
||||
else:
|
||||
normalizer = None
|
||||
|
||||
# export to JIT and ONNX
|
||||
export_policy_as_jit(policy_nn, normalizer=normalizer, path=export_model_dir, filename="policy.pt")
|
||||
export_policy_as_onnx(policy_nn, normalizer=normalizer, path=export_model_dir, filename="policy.onnx")
|
||||
|
||||
dt = env.unwrapped.step_dt
|
||||
|
||||
# reset environment
|
||||
obs = env.get_observations()
|
||||
timestep = 0
|
||||
# simulate environment
|
||||
try:
|
||||
while True:
|
||||
start_time = time.time()
|
||||
# run everything in inference mode
|
||||
with torch.inference_mode():
|
||||
# agent stepping
|
||||
actions = policy(obs)
|
||||
# env stepping
|
||||
obs, _, dones, _ = env.step(actions)
|
||||
# reset recurrent states for episodes that have terminated
|
||||
if version.parse(installed_version) >= version.parse("4.0.0"):
|
||||
policy.reset(dones)
|
||||
else:
|
||||
policy_nn.reset(dones)
|
||||
if args_cli.video:
|
||||
timestep += 1
|
||||
if timestep == args_cli.video_length:
|
||||
break
|
||||
|
||||
sleep_time = dt - (time.time() - start_time)
|
||||
if args_cli.real_time and sleep_time > 0:
|
||||
time.sleep(sleep_time)
|
||||
|
||||
# close the simulator
|
||||
env.close()
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,234 @@
|
||||
# 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
|
||||
|
||||
"""Script to play a checkpoint of an RL agent from RSL-RL."""
|
||||
|
||||
import argparse
|
||||
import contextlib
|
||||
import importlib.metadata as metadata
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
|
||||
import gymnasium as gym
|
||||
import torch
|
||||
from packaging import version
|
||||
from rsl_rl.runners import DistillationRunner, OnPolicyRunner
|
||||
|
||||
from isaaclab.envs import DirectMARLEnvCfg, DirectRLEnvCfg, ManagerBasedRLEnvCfg
|
||||
from isaaclab.utils.assets import retrieve_file_path
|
||||
from isaaclab.utils.dict import print_dict
|
||||
from isaaclab.utils.string import list_intersection, string_to_callable
|
||||
|
||||
from isaaclab_rl.rsl_rl import (
|
||||
RslRlBaseRunnerCfg,
|
||||
RslRlVecEnvWrapper,
|
||||
export_policy_as_jit,
|
||||
export_policy_as_onnx,
|
||||
handle_deprecated_rsl_rl_cfg,
|
||||
)
|
||||
from isaaclab_rl.utils.pretrained_checkpoint import get_published_pretrained_checkpoint
|
||||
|
||||
import isaaclab_tasks # noqa: F401
|
||||
from isaaclab_tasks.utils import (
|
||||
add_launcher_args,
|
||||
get_checkpoint_path,
|
||||
launch_simulation,
|
||||
setup_preset_cli,
|
||||
)
|
||||
from isaaclab_tasks.utils.hydra import hydra_task_config
|
||||
|
||||
# local imports
|
||||
import cli_args # isort: skip
|
||||
|
||||
import dex_workbench.tasks # noqa: F401
|
||||
with contextlib.suppress(ImportError):
|
||||
import isaaclab_tasks_experimental # noqa: F401
|
||||
|
||||
# -- argparse ----------------------------------------------------------------
|
||||
parser = argparse.ArgumentParser(description="Play a checkpoint of an RL agent from RSL-RL.")
|
||||
parser.add_argument("--video", action="store_true", default=False, help="Record videos during play.")
|
||||
parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).")
|
||||
parser.add_argument(
|
||||
"--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
|
||||
)
|
||||
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(
|
||||
"--agent", type=str, default="rsl_rl_cfg_entry_point", help="Name of the RL agent configuration entry point."
|
||||
)
|
||||
parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
|
||||
parser.add_argument(
|
||||
"--use_pretrained_checkpoint",
|
||||
action="store_true",
|
||||
help="Use the pre-trained checkpoint from Nucleus.",
|
||||
)
|
||||
parser.add_argument("--real-time", action="store_true", default=False, help="Run in real-time, if possible.")
|
||||
parser.add_argument("--external_callback", default=None, help="Fully qualified path to an externally defined callback.")
|
||||
cli_args.add_rsl_rl_args(parser)
|
||||
add_launcher_args(parser)
|
||||
args_cli, remaining_args = setup_preset_cli(parser)
|
||||
|
||||
if args_cli.video:
|
||||
args_cli.enable_cameras = True
|
||||
|
||||
|
||||
# Call an external callback if requested. This gives opportunity to external code to register the environments
|
||||
# The function is expected to return a list of arguments that were not consumed by the callback.
|
||||
remaining_args_env_registration = None
|
||||
if args_cli.external_callback:
|
||||
external_callback_function = string_to_callable(args_cli.external_callback, separator=".")
|
||||
remaining_args_env_registration = external_callback_function()
|
||||
|
||||
# clear out sys.argv for Hydra
|
||||
# The remaining arguments are the arguments that were not consumed by both this scripts
|
||||
# argparser and (optionally) the external callback function.
|
||||
remaining_args = list_intersection(remaining_args, remaining_args_env_registration)
|
||||
sys.argv = [sys.argv[0]] + remaining_args
|
||||
|
||||
# Check for installed RSL-RL version
|
||||
installed_version = metadata.version("rsl-rl-lib")
|
||||
|
||||
|
||||
@hydra_task_config(args_cli.task, args_cli.agent)
|
||||
def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: RslRlBaseRunnerCfg):
|
||||
"""Play with RSL-RL agent."""
|
||||
with launch_simulation(env_cfg, args_cli):
|
||||
# grab task name for checkpoint path
|
||||
task_name = args_cli.task.split(":")[-1]
|
||||
train_task_name = task_name.replace("-Play", "")
|
||||
|
||||
# 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
|
||||
|
||||
# handle deprecated configurations
|
||||
agent_cfg = handle_deprecated_rsl_rl_cfg(agent_cfg, installed_version)
|
||||
|
||||
# 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
|
||||
|
||||
# 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] Loading experiment from directory: {log_root_path}")
|
||||
if args_cli.use_pretrained_checkpoint:
|
||||
resume_path = get_published_pretrained_checkpoint("rsl_rl", train_task_name)
|
||||
if not resume_path:
|
||||
print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.")
|
||||
return
|
||||
elif args_cli.checkpoint:
|
||||
resume_path = retrieve_file_path(args_cli.checkpoint)
|
||||
else:
|
||||
resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
|
||||
|
||||
log_dir = os.path.dirname(resume_path)
|
||||
|
||||
# set the log directory for the environment
|
||||
env_cfg.log_dir = 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.cfg, DirectMARLEnvCfg):
|
||||
from isaaclab.envs import multi_agent_to_single_agent
|
||||
|
||||
env = multi_agent_to_single_agent(env)
|
||||
|
||||
# wrap for video recording
|
||||
if args_cli.video:
|
||||
video_kwargs = {
|
||||
"video_folder": os.path.join(log_dir, "videos", "play"),
|
||||
"step_trigger": lambda step: step == 0,
|
||||
"video_length": args_cli.video_length,
|
||||
"disable_logger": True,
|
||||
}
|
||||
print("[INFO] Recording videos during play.")
|
||||
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)
|
||||
|
||||
print(f"[INFO]: Loading model checkpoint from: {resume_path}")
|
||||
# load previously trained model
|
||||
if agent_cfg.class_name == "OnPolicyRunner":
|
||||
runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=None, device=agent_cfg.device)
|
||||
elif agent_cfg.class_name == "DistillationRunner":
|
||||
runner = DistillationRunner(env, agent_cfg.to_dict(), log_dir=None, device=agent_cfg.device)
|
||||
else:
|
||||
raise ValueError(f"Unsupported runner class: {agent_cfg.class_name}")
|
||||
runner.load(resume_path)
|
||||
|
||||
# obtain the trained policy for inference
|
||||
policy = runner.get_inference_policy(device=env.unwrapped.device)
|
||||
|
||||
# export the trained policy to JIT and ONNX formats
|
||||
export_model_dir = os.path.join(os.path.dirname(resume_path), "exported")
|
||||
|
||||
if version.parse(installed_version) >= version.parse("4.0.0"):
|
||||
# use the new export functions for rsl-rl >= 4.0.0
|
||||
runner.export_policy_to_jit(path=export_model_dir, filename="policy.pt")
|
||||
runner.export_policy_to_onnx(path=export_model_dir, filename="policy.onnx")
|
||||
policy_nn = None # Not needed for rsl-rl >= 4.0.0
|
||||
else:
|
||||
# extract the neural network for rsl-rl < 4.0.0
|
||||
if version.parse(installed_version) >= version.parse("2.3.0"):
|
||||
policy_nn = runner.alg.policy
|
||||
else:
|
||||
policy_nn = runner.alg.actor_critic
|
||||
|
||||
# extract the normalizer
|
||||
if hasattr(policy_nn, "actor_obs_normalizer"):
|
||||
normalizer = policy_nn.actor_obs_normalizer
|
||||
elif hasattr(policy_nn, "student_obs_normalizer"):
|
||||
normalizer = policy_nn.student_obs_normalizer
|
||||
else:
|
||||
normalizer = None
|
||||
|
||||
# export to JIT and ONNX
|
||||
export_policy_as_jit(policy_nn, normalizer=normalizer, path=export_model_dir, filename="policy.pt")
|
||||
export_policy_as_onnx(policy_nn, normalizer=normalizer, path=export_model_dir, filename="policy.onnx")
|
||||
|
||||
dt = env.unwrapped.step_dt
|
||||
|
||||
# reset environment
|
||||
obs = env.get_observations()
|
||||
timestep = 0
|
||||
# simulate environment
|
||||
try:
|
||||
while True:
|
||||
start_time = time.time()
|
||||
# run everything in inference mode
|
||||
with torch.inference_mode():
|
||||
# agent stepping
|
||||
actions = policy(obs)
|
||||
# env stepping
|
||||
obs, _, dones, _ = env.step(actions)
|
||||
# reset recurrent states for episodes that have terminated
|
||||
if version.parse(installed_version) >= version.parse("4.0.0"):
|
||||
policy.reset(dones)
|
||||
else:
|
||||
policy_nn.reset(dones)
|
||||
if args_cli.video:
|
||||
timestep += 1
|
||||
if timestep == args_cli.video_length:
|
||||
break
|
||||
|
||||
sleep_time = dt - (time.time() - start_time)
|
||||
if args_cli.real_time and sleep_time > 0:
|
||||
time.sleep(sleep_time)
|
||||
|
||||
# close the simulator
|
||||
env.close()
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,255 @@
|
||||
# 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
|
||||
|
||||
"""Script to train RL agent with RSL-RL."""
|
||||
|
||||
import warnings
|
||||
|
||||
warnings.warn(
|
||||
"scripts/reinforcement_learning/rsl_rl/train.py is deprecated. Use "
|
||||
"`./isaaclab.sh train --rl_library rsl_rl --task <TASK>` instead. "
|
||||
"Example: `./isaaclab.sh train --rl_library rsl_rl --task Isaac-Cartpole-v0`.",
|
||||
DeprecationWarning,
|
||||
stacklevel=1,
|
||||
)
|
||||
|
||||
import argparse
|
||||
import contextlib
|
||||
import importlib.metadata as metadata
|
||||
import logging
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
import time
|
||||
from datetime import datetime
|
||||
|
||||
import gymnasium as gym
|
||||
import torch
|
||||
from packaging import version
|
||||
from rsl_rl.runners import DistillationRunner, OnPolicyRunner
|
||||
|
||||
from isaaclab.envs import DirectMARLEnvCfg, DirectRLEnvCfg, ManagerBasedRLEnvCfg
|
||||
from isaaclab.utils.dict import print_dict
|
||||
from isaaclab.utils.io import dump_yaml
|
||||
from isaaclab.utils.seed import configure_seed
|
||||
from isaaclab.utils.string import list_intersection, string_to_callable
|
||||
|
||||
from isaaclab_rl.rsl_rl import RslRlBaseRunnerCfg, RslRlVecEnvWrapper, handle_deprecated_rsl_rl_cfg
|
||||
|
||||
import isaaclab_tasks # noqa: F401
|
||||
from isaaclab_tasks.utils import (
|
||||
add_launcher_args,
|
||||
get_checkpoint_path,
|
||||
launch_simulation,
|
||||
setup_preset_cli,
|
||||
)
|
||||
from isaaclab_tasks.utils.hydra import hydra_task_config
|
||||
|
||||
# local imports
|
||||
import cli_args # isort: skip
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
import dex_workbench.tasks # noqa: F401
|
||||
with contextlib.suppress(ImportError):
|
||||
import isaaclab_tasks_experimental # noqa: F401
|
||||
|
||||
RSL_RL_VERSION = "5.0.1"
|
||||
|
||||
torch.backends.cuda.matmul.allow_tf32 = True
|
||||
torch.backends.cudnn.allow_tf32 = True
|
||||
torch.backends.cudnn.deterministic = False
|
||||
torch.backends.cudnn.benchmark = False
|
||||
|
||||
# -- argparse ----------------------------------------------------------------
|
||||
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(
|
||||
"--agent", type=str, default="rsl_rl_cfg_entry_point", help="Name of the RL agent configuration entry point."
|
||||
)
|
||||
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."
|
||||
)
|
||||
parser.add_argument("--export_io_descriptors", action="store_true", default=False, help="Export IO descriptors.")
|
||||
parser.add_argument(
|
||||
"--ray-proc-id", "-rid", type=int, default=None, help="Automatically configured by Ray integration, otherwise None."
|
||||
)
|
||||
parser.add_argument("--external_callback", default=None, help="Fully qualified path to an externally defined callback.")
|
||||
cli_args.add_rsl_rl_args(parser)
|
||||
add_launcher_args(parser)
|
||||
args_cli, remaining_args = setup_preset_cli(parser)
|
||||
|
||||
if args_cli.video:
|
||||
args_cli.enable_cameras = True
|
||||
|
||||
|
||||
# Call an external callback if requested. This gives opportunity to external code to register the environments
|
||||
# The function is expected to return a list of arguments that were not consumed by the callback.
|
||||
remaining_args_env_registration = None
|
||||
if args_cli.external_callback:
|
||||
external_callback_function = string_to_callable(args_cli.external_callback, separator=".")
|
||||
remaining_args_env_registration = external_callback_function()
|
||||
|
||||
# clear out sys.argv for Hydra
|
||||
# The remaining arguments are the arguments that were not consumed by both this scripts
|
||||
# argparser and (optionally) the external callback function. Both sides of this
|
||||
# intersection share the same token vocabulary (the callback reads the user's
|
||||
# original sys.argv), so preset tokens like ``physics=NAME`` compare correctly.
|
||||
remaining_args = list_intersection(remaining_args, remaining_args_env_registration)
|
||||
sys.argv = [sys.argv[0]] + remaining_args
|
||||
|
||||
# -- check RSL-RL version ----------------------------------------------------
|
||||
installed_version = metadata.version("rsl-rl-lib")
|
||||
if 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)
|
||||
|
||||
|
||||
@hydra_task_config(args_cli.task, args_cli.agent)
|
||||
def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: RslRlBaseRunnerCfg):
|
||||
"""Train with RSL-RL agent."""
|
||||
with launch_simulation(env_cfg, args_cli):
|
||||
# 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
|
||||
)
|
||||
|
||||
# handle deprecated configurations
|
||||
agent_cfg = handle_deprecated_rsl_rl_cfg(agent_cfg, installed_version)
|
||||
|
||||
# set the environment seed
|
||||
# note: certain randomizations occur in the environment initialization so we set the seed here
|
||||
env_cfg.seed = agent_cfg.seed
|
||||
# For distributed training, launch_simulation() already resolved the
|
||||
# correct per-rank device; only apply a CLI --device override for
|
||||
# non-distributed runs (the default "cuda:0" would clobber the
|
||||
# per-rank device otherwise).
|
||||
if not args_cli.distributed:
|
||||
env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
|
||||
# check for invalid combination of CPU device with distributed training
|
||||
if args_cli.distributed and args_cli.device is not None and "cpu" in args_cli.device:
|
||||
raise ValueError(
|
||||
"Distributed training is not supported when using CPU device. "
|
||||
"Please use GPU device (e.g., --device cuda) for distributed training."
|
||||
)
|
||||
|
||||
# multi-gpu training configuration
|
||||
if args_cli.distributed:
|
||||
global_rank = int(os.getenv("RANK", "0"))
|
||||
# env_cfg.sim.device is resolved by launch_simulation() which
|
||||
# accounts for CUDA_VISIBLE_DEVICES restrictions.
|
||||
agent_cfg.device = env_cfg.sim.device
|
||||
|
||||
# use global rank for seed diversity across all nodes
|
||||
seed = agent_cfg.seed + global_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)
|
||||
|
||||
# set the IO descriptors export flag if requested
|
||||
if isinstance(env_cfg, ManagerBasedRLEnvCfg):
|
||||
env_cfg.export_io_descriptors = args_cli.export_io_descriptors
|
||||
else:
|
||||
logger.warning(
|
||||
"IO descriptors are only supported for manager based RL environments."
|
||||
" No IO descriptors will be exported."
|
||||
)
|
||||
|
||||
# set the log directory for the environment (works for all environment types)
|
||||
env_cfg.log_dir = 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.cfg, DirectMARLEnvCfg):
|
||||
from isaaclab.envs import multi_agent_to_single_agent
|
||||
|
||||
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)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
# wrap around environment for rsl-rl
|
||||
env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions)
|
||||
|
||||
# create runner from rsl-rl
|
||||
if agent_cfg.class_name == "OnPolicyRunner":
|
||||
runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device)
|
||||
elif agent_cfg.class_name == "DistillationRunner":
|
||||
runner = DistillationRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device)
|
||||
else:
|
||||
raise ValueError(f"Unsupported runner class: {agent_cfg.class_name}")
|
||||
# configure_seed must be called after runner construction so that PyTorch deterministic settings
|
||||
# do not interfere with the runner's internal initialization.
|
||||
if args_cli.deterministic:
|
||||
configure_seed(env_cfg.seed, True)
|
||||
# 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)
|
||||
|
||||
# run training
|
||||
try:
|
||||
runner.learn(num_learning_iterations=agent_cfg.max_iterations, init_at_random_ep_len=True)
|
||||
print(f"Training time: {round(time.time() - start_time, 2)} seconds")
|
||||
# close the simulator
|
||||
env.close()
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,183 @@
|
||||
# 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
|
||||
|
||||
"""RSL-RL training logic for the unified reinforcement learning entrypoint."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import contextlib
|
||||
import importlib.metadata as metadata
|
||||
import logging
|
||||
import os
|
||||
import platform
|
||||
import time
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
from common import (
|
||||
add_common_train_args,
|
||||
add_isaaclab_launcher_args,
|
||||
apply_env_overrides,
|
||||
configure_io_descriptors,
|
||||
create_isaaclab_env,
|
||||
dump_train_configs,
|
||||
enable_cameras_for_video,
|
||||
import_local_module,
|
||||
set_hydra_args,
|
||||
validate_distributed_device,
|
||||
wrap_record_video,
|
||||
)
|
||||
from packaging import version
|
||||
|
||||
import isaaclab_tasks # noqa: F401
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
RSL_RL_VERSION = "5.0.1"
|
||||
RL_ROOT = Path(__file__).resolve().parents[1]
|
||||
CLI_ARGS = import_local_module("isaaclab_rsl_rl_cli_args", RL_ROOT / "rsl_rl" / "cli_args.py")
|
||||
|
||||
import dex_workbench.tasks # noqa: F401
|
||||
with contextlib.suppress(ImportError):
|
||||
import isaaclab_tasks_experimental # noqa: F401
|
||||
|
||||
|
||||
def _check_rsl_rl_version() -> str:
|
||||
"""Check that the installed RSL-RL version is supported."""
|
||||
installed_version = metadata.version("rsl-rl-lib")
|
||||
if 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"
|
||||
)
|
||||
raise SystemExit(1)
|
||||
return installed_version
|
||||
|
||||
|
||||
def _parse_args(argv: list[str]) -> argparse.Namespace:
|
||||
"""Parse RSL-RL training arguments."""
|
||||
from isaaclab.utils.string import list_intersection, string_to_callable
|
||||
|
||||
from isaaclab_tasks.utils import setup_preset_cli
|
||||
|
||||
parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.")
|
||||
add_common_train_args(
|
||||
parser,
|
||||
agent_default="rsl_rl_cfg_entry_point",
|
||||
agent_help="Name of the RL agent configuration entry point.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--external_callback",
|
||||
default=None,
|
||||
help="Fully qualified path to an externally defined callback.",
|
||||
)
|
||||
CLI_ARGS.add_rsl_rl_args(parser)
|
||||
add_isaaclab_launcher_args(parser)
|
||||
# setup_preset_cli registers preset-selection help text + runs parse_known_args
|
||||
args_cli, remaining_args = setup_preset_cli(parser, argv)
|
||||
enable_cameras_for_video(args_cli)
|
||||
|
||||
remaining_args_env_registration = None
|
||||
if args_cli.external_callback:
|
||||
external_callback_function = string_to_callable(args_cli.external_callback, separator=".")
|
||||
remaining_args_env_registration = external_callback_function()
|
||||
|
||||
# physics=/renderer=/presets= tokens pass through the remainder for hydra to parse later
|
||||
set_hydra_args(list_intersection(remaining_args, remaining_args_env_registration))
|
||||
return args_cli
|
||||
|
||||
|
||||
def run(argv: list[str]) -> None:
|
||||
"""Train an RSL-RL agent."""
|
||||
import torch
|
||||
from rsl_rl.runners import DistillationRunner, OnPolicyRunner
|
||||
|
||||
from isaaclab.envs import DirectMARLEnvCfg
|
||||
|
||||
from isaaclab_rl.rsl_rl import RslRlVecEnvWrapper, handle_deprecated_rsl_rl_cfg
|
||||
|
||||
from isaaclab_tasks.utils import get_checkpoint_path, launch_simulation, resolve_task_config
|
||||
|
||||
torch.backends.cuda.matmul.allow_tf32 = True
|
||||
torch.backends.cudnn.allow_tf32 = True
|
||||
torch.backends.cudnn.deterministic = False
|
||||
torch.backends.cudnn.benchmark = False
|
||||
|
||||
args_cli = _parse_args(argv)
|
||||
installed_version = _check_rsl_rl_version()
|
||||
env_cfg, agent_cfg = resolve_task_config(args_cli.task, args_cli.agent)
|
||||
|
||||
with launch_simulation(env_cfg, args_cli):
|
||||
agent_cfg = CLI_ARGS.update_rsl_rl_cfg(agent_cfg, args_cli)
|
||||
apply_env_overrides(args_cli, env_cfg)
|
||||
agent_cfg.max_iterations = (
|
||||
args_cli.max_iterations if args_cli.max_iterations is not None else agent_cfg.max_iterations
|
||||
)
|
||||
|
||||
agent_cfg = handle_deprecated_rsl_rl_cfg(agent_cfg, installed_version)
|
||||
|
||||
env_cfg.seed = agent_cfg.seed
|
||||
validate_distributed_device(args_cli)
|
||||
|
||||
if args_cli.distributed:
|
||||
global_rank = int(os.getenv("RANK", "0"))
|
||||
agent_cfg.device = env_cfg.sim.device
|
||||
|
||||
seed = agent_cfg.seed + global_rank
|
||||
env_cfg.seed = seed
|
||||
agent_cfg.seed = seed
|
||||
|
||||
log_root_path = os.path.abspath(os.path.join("logs", "rsl_rl", agent_cfg.experiment_name))
|
||||
print(f"[INFO] Logging experiment in directory: {log_root_path}")
|
||||
log_dir = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
|
||||
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)
|
||||
|
||||
configure_io_descriptors(env_cfg, args_cli, logger)
|
||||
env_cfg.log_dir = log_dir
|
||||
|
||||
env = create_isaaclab_env(
|
||||
args_cli.task,
|
||||
env_cfg,
|
||||
args_cli,
|
||||
convert_marl_to_single_agent=isinstance(env_cfg, DirectMARLEnvCfg),
|
||||
)
|
||||
|
||||
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)
|
||||
|
||||
env = wrap_record_video(env, log_dir, args_cli)
|
||||
|
||||
start_time = time.time()
|
||||
env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions)
|
||||
|
||||
if agent_cfg.class_name == "OnPolicyRunner":
|
||||
runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device)
|
||||
elif agent_cfg.class_name == "DistillationRunner":
|
||||
runner = DistillationRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device)
|
||||
else:
|
||||
raise ValueError(f"Unsupported runner class: {agent_cfg.class_name}")
|
||||
|
||||
runner.add_git_repo_to_log(__file__)
|
||||
if agent_cfg.resume or agent_cfg.algorithm.class_name == "Distillation":
|
||||
print(f"[INFO]: Loading model checkpoint from: {resume_path}")
|
||||
runner.load(resume_path)
|
||||
|
||||
dump_train_configs(log_dir, env_cfg, agent_cfg)
|
||||
|
||||
try:
|
||||
runner.learn(num_learning_iterations=agent_cfg.max_iterations, init_at_random_ep_len=True)
|
||||
print(f"Training time: {round(time.time() - start_time, 2)} seconds")
|
||||
env.close()
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
@@ -0,0 +1,86 @@
|
||||
# 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
|
||||
|
||||
"""Script to run an environment with zero action agent."""
|
||||
|
||||
import argparse
|
||||
import contextlib
|
||||
import sys
|
||||
|
||||
import gymnasium as gym
|
||||
import torch
|
||||
|
||||
import isaaclab_tasks # noqa: F401
|
||||
|
||||
with contextlib.suppress(ImportError):
|
||||
import isaaclab_tasks_experimental # noqa: F401
|
||||
from isaaclab_tasks.utils import (
|
||||
add_launcher_args,
|
||||
launch_simulation,
|
||||
resolve_task_config,
|
||||
setup_preset_cli,
|
||||
)
|
||||
|
||||
# add argparse arguments
|
||||
parser = argparse.ArgumentParser(description="Zero agent for Isaac Lab environments.")
|
||||
parser.add_argument(
|
||||
"--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
|
||||
)
|
||||
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.")
|
||||
# append AppLauncher cli args
|
||||
add_launcher_args(parser)
|
||||
# simple agents should open Kit visualizer by default
|
||||
parser.set_defaults(visualizer=["kit"])
|
||||
args_cli, hydra_args = setup_preset_cli(parser)
|
||||
sys.argv = [sys.argv[0]] + hydra_args
|
||||
|
||||
import dex_workbench.tasks # noqa: F401
|
||||
MAX_STEPS = 100
|
||||
|
||||
|
||||
def main():
|
||||
"""Zero actions agent with Isaac Lab environment."""
|
||||
|
||||
torch.manual_seed(42)
|
||||
|
||||
# parse configuration via Hydra (supports preset selection, e.g. env.sim.physics=newton_mjwarp)
|
||||
env_cfg, _ = resolve_task_config(args_cli.task, "")
|
||||
|
||||
with launch_simulation(env_cfg, args_cli):
|
||||
# override with CLI arguments
|
||||
env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
|
||||
env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
|
||||
if args_cli.disable_fabric:
|
||||
env_cfg.sim.use_fabric = False
|
||||
|
||||
# create environment
|
||||
env = gym.make(args_cli.task, cfg=env_cfg)
|
||||
|
||||
# print info (this is vectorized environment)
|
||||
print(f"[INFO]: Gym observation space: {env.observation_space}")
|
||||
print(f"[INFO]: Gym action space: {env.action_space}")
|
||||
# reset environment
|
||||
env.reset()
|
||||
# simulate environment
|
||||
# keep running while any visualizer is open, otherwise fall back to MAX_STEPS
|
||||
sim = env.unwrapped.sim
|
||||
actions = torch.zeros(env.action_space.shape, device=env.unwrapped.device)
|
||||
while True:
|
||||
if sim.visualizers:
|
||||
# visualizer mode: run until the visualizer window is closed
|
||||
if not any(v.is_running() and not v.is_closed for v in sim.visualizers):
|
||||
break
|
||||
# run everything in inference mode
|
||||
with torch.inference_mode():
|
||||
# apply actions
|
||||
env.step(actions)
|
||||
# close the simulator
|
||||
env.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# run the main function
|
||||
main()
|
||||
Reference in New Issue
Block a user