Initial commit

scripts/rsl_rl/cli_args.py

@@ -0,0 +1,93 @@
+# 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
+
+from __future__ import annotations
+
+import argparse
+import random
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from isaaclab_rl.rsl_rl import RslRlBaseRunnerCfg
+
+
+def add_rsl_rl_args(parser: argparse.ArgumentParser):
+    """Add RSL-RL arguments to the parser.
+
+    Args:
+        parser: The parser to add the arguments to.
+    """
+    # create a new argument group
+    arg_group = parser.add_argument_group("rsl_rl", description="Arguments for RSL-RL agent.")
+    # -- experiment arguments
+    arg_group.add_argument(
+        "--experiment_name", type=str, default=None, help="Name of the experiment folder where logs will be stored."
+    )
+    arg_group.add_argument("--run_name", type=str, default=None, help="Run name suffix to the log directory.")
+    # -- load arguments
+    arg_group.add_argument("--resume", action="store_true", default=False, help="Whether to resume from a checkpoint.")
+    arg_group.add_argument("--load_run", type=str, default=None, help="Name of the run folder to resume from.")
+    arg_group.add_argument("--checkpoint", type=str, default=None, help="Checkpoint file to resume from.")
+    # -- logger arguments
+    arg_group.add_argument(
+        "--logger", type=str, default=None, choices={"wandb", "tensorboard", "neptune"}, help="Logger module to use."
+    )
+    arg_group.add_argument(
+        "--log_project_name", type=str, default=None, help="Name of the logging project when using wandb or neptune."
+    )
+
+
+def parse_rsl_rl_cfg(task_name: str, args_cli: argparse.Namespace) -> RslRlBaseRunnerCfg:
+    """Parse configuration for RSL-RL agent based on inputs.
+
+    Args:
+        task_name: The name of the environment.
+        args_cli: The command line arguments.
+
+    Returns:
+        The parsed configuration for RSL-RL agent based on inputs.
+    """
+    from isaaclab_tasks.utils.parse_cfg import load_cfg_from_registry
+
+    # load the default configuration
+    rslrl_cfg: RslRlBaseRunnerCfg = load_cfg_from_registry(task_name, "rsl_rl_cfg_entry_point")
+    rslrl_cfg = update_rsl_rl_cfg(rslrl_cfg, args_cli)
+    return rslrl_cfg
+
+
+def update_rsl_rl_cfg(agent_cfg: RslRlBaseRunnerCfg, args_cli: argparse.Namespace):
+    """Update configuration for RSL-RL agent based on inputs.
+
+    Args:
+        agent_cfg: The configuration for RSL-RL agent.
+        args_cli: The command line arguments.
+
+    Returns:
+        The updated configuration for RSL-RL agent based on inputs.
+    """
+    # override the default configuration with CLI arguments
+    if hasattr(args_cli, "seed") and args_cli.seed is not None:
+        # randomly sample a seed if seed = -1
+        if args_cli.seed == -1:
+            args_cli.seed = random.randint(0, 10000)
+        agent_cfg.seed = args_cli.seed
+    if args_cli.resume is not None:
+        agent_cfg.resume = args_cli.resume
+    if args_cli.load_run is not None:
+        agent_cfg.load_run = args_cli.load_run
+    if args_cli.checkpoint is not None:
+        agent_cfg.load_checkpoint = args_cli.checkpoint
+    if args_cli.experiment_name is not None:
+        agent_cfg.experiment_name = args_cli.experiment_name
+    if args_cli.run_name is not None:
+        agent_cfg.run_name = args_cli.run_name
+    if args_cli.logger is not None:
+        agent_cfg.logger = args_cli.logger
+    # set the project name for wandb and neptune
+    if agent_cfg.logger in {"wandb", "neptune"} and args_cli.log_project_name:
+        agent_cfg.wandb_project = args_cli.log_project_name
+        agent_cfg.neptune_project = args_cli.log_project_name
+
+    return agent_cfg

scripts/rsl_rl/play.py

@@ -0,0 +1,251 @@
+# 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 if an RL agent from RSL-RL."""
+
+import warnings
+
+warnings.warn(
+    "scripts/reinforcement_learning/rsl_rl/play.py is deprecated. Use "
+    "`./isaaclab.sh play --rl_library rsl_rl --task <TASK>` instead. "
+    "Example: `./isaaclab.sh play --rl_library rsl_rl --task Isaac-Cartpole-v0`.",
+    DeprecationWarning,
+    stacklevel=1,
+)
+
+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.seed import configure_seed
+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="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(
+    "--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. Both sides of this
+# intersection are pre-fold (the callback reads the user's original sys.argv), so
+# preset tokens like ``physics=NAME`` compare correctly here. Fold runs after.
+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 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()

scripts/rsl_rl/play_rsl_rl.py

@@ -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()

scripts/rsl_rl/train.py

@@ -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()

scripts/rsl_rl/train_rsl_rl.py

@@ -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