simulation/modules/IsaacLab/scripts/benchmarks/benchmark_rsl_rl.py

# 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 benchmark RL agent with RSL-RL."""

"""Launch Isaac Sim Simulator first."""

import argparse
import contextlib
import os
import sys
import time

from isaaclab.app import AppLauncher

from isaaclab_tasks.utils import setup_preset_cli

from scripts.benchmarks.early_stop import (
    RslRlEarlyStopWrapper,
    add_success_cli_args,
    build_success_kwargs,
    get_success_tracker,
)

sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../.."))
import scripts.reinforcement_learning.rsl_rl.cli_args as cli_args  # isort: skip

# add argparse arguments
parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.")
parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.")
parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).")
parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).")
parser.add_argument("--num_envs", type=int, default=4096, help="Number of environments to simulate.")
parser.add_argument("--task", type=str, default=None, help="Name of the task.")
parser.add_argument("--seed", type=int, default=42, help="Seed used for the environment")
parser.add_argument("--max_iterations", type=int, default=10, 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(
    "--benchmark_backend",
    type=str,
    default="omniperf",
    choices=[
        "json",
        "osmo",
        "omniperf",
        "summary",
        "LocalLogMetrics",
        "JSONFileMetrics",
        "OsmoKPIFile",
        "OmniPerfKPIFile",
    ],
    help="Benchmarking backend options, defaults omniperf",
)
parser.add_argument("--output_path", type=str, default=".", help="Path to output benchmark results.")
parser.add_argument(
    "--reward_threshold", type=float, default=None, help="Reward threshold for convergence (overrides config)."
)
parser.add_argument(
    "--check_convergence", action="store_true", help="Check reward convergence using thresholds from configs.yaml."
)
parser.add_argument(
    "--convergence_config", type=str, default="full", help="Config mode for convergence thresholds (default: full)."
)
add_success_cli_args(parser)

# append RSL-RL cli arguments
cli_args.add_rsl_rl_args(parser)
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
args_cli, hydra_args = setup_preset_cli(parser)
sys.argv = [sys.argv[0]] + hydra_args
if args_cli.video:
    args_cli.enable_cameras = True

imports_time_begin = time.perf_counter_ns()

import importlib.metadata as metadata
from datetime import datetime

import gymnasium as gym
import numpy as np
import torch
from rsl_rl.runners import OnPolicyRunner

from isaaclab.envs import DirectMARLEnvCfg, DirectRLEnvCfg, ManagerBasedRLEnvCfg
from isaaclab.utils.dict import print_dict
from isaaclab.utils.io import dump_yaml

from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlVecEnvWrapper, handle_deprecated_rsl_rl_cfg

import isaaclab_tasks  # noqa: F401

# PLACEHOLDER: Extension template (do not remove this comment)
with contextlib.suppress(ImportError):
    import isaaclab_tasks_experimental  # noqa: F401
from isaaclab_tasks.utils import get_checkpoint_path, launch_simulation, resolve_task_config

imports_time_end = time.perf_counter_ns()

from isaaclab.test.benchmark import BaseIsaacLabBenchmark, BenchmarkMonitor
from isaaclab.utils.timer import Timer

from scripts.benchmarks.utils import (
    get_backend_type,
    get_preset_string,
    log_app_start_time,
    log_python_imports_time,
    log_rl_training_metrics,
    log_runtime_step_times,
    log_scene_creation_time,
    log_simulation_start_time,
    log_success,
    log_task_start_time,
    log_total_start_time,
    parse_tf_logs,
)

torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = False

# Create the benchmark
backend_type = get_backend_type(args_cli.benchmark_backend)
benchmark = BaseIsaacLabBenchmark(
    benchmark_name="benchmark_rsl_rl_train",
    backend_type=backend_type,
    output_path=args_cli.output_path,
    use_recorders=True,
    frametime_recorders=backend_type in ("summary", "omniperf"),
    output_prefix=f"benchmark_rsl_rl_train_{args_cli.task}",
    workflow_metadata={
        "metadata": [
            {"name": "task", "data": args_cli.task},
            {"name": "seed", "data": args_cli.seed},
            {"name": "num_envs", "data": args_cli.num_envs},
            {"name": "max_iterations", "data": args_cli.max_iterations},
            {"name": "presets", "data": get_preset_string(hydra_args)},
        ]
    },
)


def main(
    env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg,
    agent_cfg: RslRlOnPolicyRunnerCfg,
    app_start_time_begin: int,
    app_start_time_end: int,
):
    """Train with RSL-RL agent."""
    # parse configuration
    # override configurations with non-hydra CLI arguments
    agent_cfg = cli_args.update_rsl_rl_cfg(agent_cfg, args_cli)
    env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
    agent_cfg.max_iterations = (
        args_cli.max_iterations if args_cli.max_iterations is not None else agent_cfg.max_iterations
    )

    # set the environment seed
    # note: certain randomizations occur in the environment initialization so we set the seed here
    env_cfg.seed = agent_cfg.seed
    # 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
    # env_cfg.sim.device is already resolved by launch_simulation().
    world_rank = 0
    world_size = 1
    if args_cli.distributed:
        agent_cfg.device = env_cfg.sim.device

        # use global rank for seed diversity across all nodes
        world_rank = int(os.getenv("RANK", "0"))
        seed = agent_cfg.seed + world_rank
        env_cfg.seed = seed
        agent_cfg.seed = seed
        world_size = int(os.getenv("WORLD_SIZE", 1))

    # 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")
    if agent_cfg.run_name:
        log_dir += f"_{agent_cfg.run_name}"
    log_dir = os.path.join(log_root_path, log_dir)

    # max iterations for training
    if args_cli.max_iterations:
        agent_cfg.max_iterations = args_cli.max_iterations

    task_startup_time_begin = time.perf_counter_ns()

    # create isaac environment
    env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
    # wrap for video recording
    if args_cli.video:
        video_kwargs = {
            "video_folder": os.path.join(log_dir, "videos"),
            "step_trigger": lambda step: step % args_cli.video_interval == 0,
            "video_length": args_cli.video_length,
            "disable_logger": True,
        }
        print("[INFO] Recording videos during training.")
        print_dict(video_kwargs, nesting=4)
        env = gym.wrappers.RecordVideo(env, **video_kwargs)
    # wrap around environment for rsl-rl
    env = RslRlVecEnvWrapper(env)

    task_startup_time_end = time.perf_counter_ns()

    # handle deprecated configurations (e.g. legacy policy -> actor/critic migration)
    agent_cfg = handle_deprecated_rsl_rl_cfg(agent_cfg, metadata.version("rsl-rl-lib"))

    # create runner from rsl-rl
    runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device)
    # write git state to logs
    runner.add_git_repo_to_log(__file__)
    # save resume path before creating a new log_dir
    if agent_cfg.resume:
        # get path to previous checkpoint
        resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
        print(f"[INFO]: Loading model checkpoint from: {resume_path}")
        # load previously trained model
        runner.load(resume_path)

    # set seed of the environment
    env.seed(agent_cfg.seed)

    # 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)

    # always track the success metric; early-stop only if --check_success
    early_stop_ctx = RslRlEarlyStopWrapper(
        env, runner, num_steps_per_env=agent_cfg.num_steps_per_env, **build_success_kwargs(args_cli)
    )

    # run training with continuous benchmark monitoring
    with early_stop_ctx, BenchmarkMonitor(benchmark, interval=1.0):
        runner.learn(num_learning_iterations=agent_cfg.max_iterations, init_at_random_ep_len=True)

    if world_rank == 0:
        # Final update after training completes
        benchmark.update_manual_recorders()

        # parse tensorboard file stats
        log_data = parse_tf_logs(log_dir)

        # prepare RL timing dict
        collection_fps = (
            1
            / (np.array(log_data["Perf/collection_time"]))
            * env.unwrapped.num_envs
            * agent_cfg.num_steps_per_env
            * world_size
        )
        rl_training_times = {
            "Collection Time": (np.array(log_data["Perf/collection_time"]) / 1000).tolist(),
            "Learning Time": (np.array(log_data["Perf/learning_time"]) / 1000).tolist(),
            "Collection FPS": collection_fps.tolist(),
            "Total FPS": log_data["Perf/total_fps"] * world_size,
        }

        # log additional metrics to benchmark services
        log_app_start_time(benchmark, (app_start_time_end - app_start_time_begin) / 1e6)
        log_python_imports_time(benchmark, (imports_time_end - imports_time_begin) / 1e6)
        log_task_start_time(benchmark, (task_startup_time_end - task_startup_time_begin) / 1e6)
        log_scene_creation_time(benchmark, Timer.get_timer_info("scene_creation") * 1000)
        log_simulation_start_time(benchmark, Timer.get_timer_info("simulation_start") * 1000)
        log_total_start_time(benchmark, (task_startup_time_end - app_start_time_begin) / 1e6)
        log_runtime_step_times(benchmark, rl_training_times, compute_stats=True)
        log_rl_training_metrics(
            benchmark,
            log_data,
            reward_tag="Train/mean_reward",
            episode_length_tag="Train/mean_episode_length",
            task=args_cli.task,
            workflow="rsl_rl",
            should_check_convergence=args_cli.check_convergence,
            reward_threshold=args_cli.reward_threshold,
            convergence_config=args_cli.convergence_config,
        )

        tracker = get_success_tracker(args_cli, early_stop_ctx.tracker, log_data)
        log_success(benchmark, tracker, framework_iteration_count=early_stop_ctx.framework_iteration_count)

        benchmark._finalize_impl()

    # close the simulator
    env.close()


if __name__ == "__main__":
    env_cfg, agent_cfg = resolve_task_config(args_cli.task, "rsl_rl_cfg_entry_point")

    app_start_time_begin = time.perf_counter_ns()
    with launch_simulation(env_cfg, args_cli):
        app_start_time_end = time.perf_counter_ns()
        main(env_cfg, agent_cfg, app_start_time_begin, app_start_time_end)