Updated some changes of training scripts from IsaacLab

CONTRIBUTORS.md

@@ -24,7 +24,7 @@ Guidelines for modifications:
 * [Zhenbiao Huang](https://github.com/legubiao)
 * [Zhihao Liu](https://github.com/Luo1imasi)
 * [Zsibot](https://www.zsibot.com)
-
+* [Linden713](https://github.com/linden713)
 ## Acknowledgements
 
 * Shiyu Zhou

scripts/reinforcement_learning/rsl_rl/cli_args.py

@@ -1,10 +1,10 @@
 # Copyright (c) 2024-2025 Ziqi Fan
 # SPDX-License-Identifier: Apache-2.0
 
-# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
 # All rights reserved.
 #
-# SPDX-License-Identifier: Apache-2.0
+# SPDX-License-Identifier: BSD-3-Clause
 
 from __future__ import annotations
 

scripts/reinforcement_learning/rsl_rl/play.py

@@ -1,10 +1,10 @@
 # Copyright (c) 2024-2025 Ziqi Fan
 # SPDX-License-Identifier: Apache-2.0
 
-# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
 # All rights reserved.
 #
-# SPDX-License-Identifier: Apache-2.0
+# SPDX-License-Identifier: BSD-3-Clause
 
 """Script to play a checkpoint if an RL agent from RSL-RL."""
 
@@ -78,7 +78,10 @@
 from isaaclab.utils.assets import retrieve_file_path
 from isaaclab.utils.dict import print_dict
 from isaaclab.utils.pretrained_checkpoint import get_published_pretrained_checkpoint
+
 from isaaclab_rl.rsl_rl import RslRlBaseRunnerCfg, RslRlVecEnvWrapper, export_policy_as_jit, export_policy_as_onnx
+
+import isaaclab_tasks  # noqa: F401
 from isaaclab_tasks.utils import get_checkpoint_path
 from isaaclab_tasks.utils.hydra import hydra_task_config
 
@@ -90,10 +93,11 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     """Play with RSL-RL agent."""
     # 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: RslRlBaseRunnerCfg = 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 64
+    env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
 
     # set the environment seed
     # note: certain randomizations occur in the environment initialization so we set the seed here
@@ -134,7 +138,7 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     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", task_name)
+        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
@@ -217,7 +221,6 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
         with torch.inference_mode():
             # agent stepping
             actions = policy(obs)
-            # actions = torch.zeros_like(actions)
             # env stepping
             obs, _, _, _ = env.step(actions)
         if args_cli.video:

scripts/reinforcement_learning/rsl_rl/train.py

@@ -1,17 +1,16 @@
 # Copyright (c) 2024-2025 Ziqi Fan
 # SPDX-License-Identifier: Apache-2.0
 
-# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
 # All rights reserved.
 #
-# SPDX-License-Identifier: Apache-2.0
+# SPDX-License-Identifier: BSD-3-Clause
 
 """Script to train RL agent with RSL-RL."""
 
 """Launch Isaac Sim Simulator first."""
 
 import argparse
-import os
 import sys
 
 from isaaclab.app import AppLauncher
@@ -55,14 +54,18 @@
 """Check for minimum supported RSL-RL version."""
 
 import importlib.metadata as metadata
+import platform
 
 from packaging import version
 
 # check minimum supported rsl-rl version
 RSL_RL_VERSION = "3.0.1"
 installed_version = metadata.version("rsl-rl-lib")
 if version.parse(installed_version) < version.parse(RSL_RL_VERSION):
-    cmd = [r"python", "-m", "pip", "install", f"rsl-rl-lib=={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:"
@@ -73,6 +76,7 @@
 """Rest everything follows."""
 
 import gymnasium as gym
+import os
 import torch
 from datetime import datetime
 
@@ -87,8 +91,11 @@
     multi_agent_to_single_agent,
 )
 from isaaclab.utils.dict import print_dict
-from isaaclab.utils.io import dump_pickle, dump_yaml
+from isaaclab.utils.io import dump_yaml
+
 from isaaclab_rl.rsl_rl import RslRlBaseRunnerCfg, RslRlVecEnvWrapper
+
+import isaaclab_tasks  # noqa: F401
 from isaaclab_tasks.utils import get_checkpoint_path
 from isaaclab_tasks.utils.hydra import hydra_task_config
 
@@ -192,8 +199,6 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     # 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)
-    dump_pickle(os.path.join(log_dir, "params", "env.pkl"), env_cfg)
-    dump_pickle(os.path.join(log_dir, "params", "agent.pkl"), agent_cfg)
 
     # run training
     runner.learn(num_learning_iterations=agent_cfg.max_iterations, init_at_random_ep_len=True)

scripts/reinforcement_learning/skrl/play.py

@@ -1,10 +1,10 @@
 # Copyright (c) 2024-2025 Ziqi Fan
 # SPDX-License-Identifier: Apache-2.0
 
-# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
 # All rights reserved.
 #
-# SPDX-License-Identifier: Apache-2.0
+# SPDX-License-Identifier: BSD-3-Clause
 
 """
 Script to play a checkpoint of an RL agent from skrl.
@@ -71,7 +71,6 @@
 
 # clear out sys.argv for Hydra
 sys.argv = [sys.argv[0]] + hydra_args
-
 # launch omniverse app
 app_launcher = AppLauncher(args_cli)
 simulation_app = app_launcher.app
@@ -88,7 +87,7 @@
 from packaging import version
 
 # check for minimum supported skrl version
-SKRL_VERSION = "1.4.2"
+SKRL_VERSION = "1.4.3"
 if version.parse(skrl.__version__) < version.parse(SKRL_VERSION):
     skrl.logger.error(
         f"Unsupported skrl version: {skrl.__version__}. "
@@ -110,7 +109,10 @@
 )
 from isaaclab.utils.dict import print_dict
 from isaaclab.utils.pretrained_checkpoint import get_published_pretrained_checkpoint
+
 from isaaclab_rl.skrl import SkrlVecEnvWrapper
+
+import isaaclab_tasks  # noqa: F401
 from isaaclab_tasks.utils import get_checkpoint_path
 from isaaclab_tasks.utils.hydra import hydra_task_config
 
@@ -122,11 +124,16 @@
     agent_cfg_entry_point = "skrl_cfg_entry_point" if algorithm in ["ppo"] else f"skrl_{algorithm}_cfg_entry_point"
 else:
     agent_cfg_entry_point = args_cli.agent
+    algorithm = agent_cfg_entry_point.split("_cfg")[0].split("skrl_")[-1].lower()
 
 
 @hydra_task_config(args_cli.task, agent_cfg_entry_point)
 def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, experiment_cfg: dict):
     """Play with skrl agent."""
+    # 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
     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
@@ -135,7 +142,7 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, expe
     if args_cli.ml_framework.startswith("jax"):
         skrl.config.jax.backend = "jax" if args_cli.ml_framework == "jax" else "numpy"
 
-    # randomly sample a seed if seed = -1
+        # randomly sample a seed if seed = -1
     if args_cli.seed == -1:
         args_cli.seed = random.randint(0, 10000)
 
@@ -144,15 +151,13 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, expe
     experiment_cfg["seed"] = args_cli.seed if args_cli.seed is not None else experiment_cfg["seed"]
     env_cfg.seed = experiment_cfg["seed"]
 
-    task_name = args_cli.task.split(":")[-1]
-
     # specify directory for logging experiments (load checkpoint)
     log_root_path = os.path.join("logs", "skrl", experiment_cfg["agent"]["experiment"]["directory"])
     log_root_path = os.path.abspath(log_root_path)
     print(f"[INFO] Loading experiment from directory: {log_root_path}")
     # get checkpoint path
     if args_cli.use_pretrained_checkpoint:
-        resume_path = get_published_pretrained_checkpoint("skrl", task_name)
+        resume_path = get_published_pretrained_checkpoint("skrl", train_task_name)
         if not resume_path:
             print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.")
             return

scripts/reinforcement_learning/skrl/train.py

@@ -1,10 +1,10 @@
 # Copyright (c) 2024-2025 Ziqi Fan
 # SPDX-License-Identifier: Apache-2.0
 
-# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
 # All rights reserved.
 #
-# SPDX-License-Identifier: Apache-2.0
+# SPDX-License-Identifier: BSD-3-Clause
 
 """
 Script to train RL agent with skrl.
@@ -107,18 +107,22 @@
 )
 from isaaclab.utils.assets import retrieve_file_path
 from isaaclab.utils.dict import print_dict
-from isaaclab.utils.io import dump_pickle, dump_yaml
+from isaaclab.utils.io import dump_yaml
+
 from isaaclab_rl.skrl import SkrlVecEnvWrapper
+
+import isaaclab_tasks  # noqa: F401
 from isaaclab_tasks.utils.hydra import hydra_task_config
 
-import robot_lab  # noqa: F401
+import robot_lab # noqa: F401
 
 # config shortcuts
 if args_cli.agent is None:
     algorithm = args_cli.algorithm.lower()
     agent_cfg_entry_point = "skrl_cfg_entry_point" if algorithm in ["ppo"] else f"skrl_{algorithm}_cfg_entry_point"
 else:
     agent_cfg_entry_point = args_cli.agent
+    algorithm = agent_cfg_entry_point.split("_cfg")[0].split("skrl_")[-1].lower()
 
 
 @hydra_task_config(args_cli.task, agent_cfg_entry_point)
@@ -167,8 +171,6 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     # 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)
-    dump_pickle(os.path.join(log_dir, "params", "env.pkl"), env_cfg)
-    dump_pickle(os.path.join(log_dir, "params", "agent.pkl"), agent_cfg)
 
     # get checkpoint path (to resume training)
     resume_path = retrieve_file_path(args_cli.checkpoint) if args_cli.checkpoint else None

scripts/tools/sync_training_scripts.sh

@@ -0,0 +1,36 @@
+#!/usr/bin/env bash
+# Sync skrl and rsl_rl train/play scripts from Isaac Lab releases and reapply local tweaks.
+set -euo pipefail
+
+fetch() {
+  local url="$1"
+  local dest="$2"
+  echo "Fetching ${url} to ${dest}"
+  curl -fsSL "${url}" -o "${dest}"
+}
+
+# Sync for skrl
+SKRL_BASE_URL="https://raw.githubusercontent.com/isaac-sim/IsaacLab/main/scripts/reinforcement_learning/skrl"
+SKRL_DEST_DIR="scripts/reinforcement_learning/skrl"
+SKRL_FILES=(train.py play.py)
+
+echo "Syncing skrl scripts..."
+for name in "${SKRL_FILES[@]}"; do
+  url="${SKRL_BASE_URL}/${name}"
+  dest_path="${SKRL_DEST_DIR}/${name}"
+  fetch "${url}" "${dest_path}"
+done
+
+# Sync for rsl_rl
+RSL_RL_BASE_URL="https://raw.githubusercontent.com/isaac-sim/IsaacLab/main/scripts/reinforcement_learning/rsl_rl"
+RSL_RL_DEST_DIR="scripts/reinforcement_learning/rsl_rl"
+RSL_RL_FILES=(train.py play.py cli_args.py)
+
+echo "Syncing rsl_rl scripts..."
+for name in "${RSL_RL_FILES[@]}"; do
+  url="${RSL_RL_BASE_URL}/${name}"
+  dest_path="${RSL_RL_DEST_DIR}/${name}"
+  fetch "${url}" "${dest_path}"
+done
+
+echo "Done."