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scripts/rl_games/train.py

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+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Script to train RL agent with RL-Games."""
+
+"""Launch Isaac Sim Simulator first."""
+
+import argparse
+import sys
+
+from isaaclab.app import AppLauncher
+
+# add argparse arguments
+parser = argparse.ArgumentParser(description="Train an RL agent with RL-Games.")
+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("--seed", type=int, default=None, help="Seed used for the environment")
+parser.add_argument(
+    "--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes."
+)
+parser.add_argument("--checkpoint", type=str, default=None, help="Path to model checkpoint.")
+parser.add_argument("--sigma", type=str, default=None, help="The policy's initial standard deviation.")
+parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.")
+
+# append AppLauncher cli args
+AppLauncher.add_app_launcher_args(parser)
+# parse the arguments
+args_cli, hydra_args = parser.parse_known_args()
+# always enable cameras to record video
+if args_cli.video:
+    args_cli.enable_cameras = True
+
+# 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
+
+"""Rest everything follows."""
+
+import gymnasium as gym
+import math
+import os
+import random
+from datetime import datetime
+
+from rl_games.common import env_configurations, vecenv
+from rl_games.common.algo_observer import IsaacAlgoObserver
+from rl_games.torch_runner import Runner
+
+from isaaclab.envs import (
+    DirectMARLEnv,
+    DirectMARLEnvCfg,
+    DirectRLEnvCfg,
+    ManagerBasedRLEnvCfg,
+    multi_agent_to_single_agent,
+)
+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_rl.rl_games import RlGamesGpuEnv, RlGamesVecEnvWrapper
+
+import isaaclab_tasks  # noqa: F401
+from isaaclab_tasks.utils.hydra import hydra_task_config
+
+import FLEXR_v0.tasks  # noqa: F401
+
+
+@hydra_task_config(args_cli.task, "rl_games_cfg_entry_point")
+def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict):
+    """Train with RL-Games agent."""
+    # 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
+
+    # randomly sample a seed if seed = -1
+    if args_cli.seed == -1:
+        args_cli.seed = random.randint(0, 10000)
+
+    agent_cfg["params"]["seed"] = args_cli.seed if args_cli.seed is not None else agent_cfg["params"]["seed"]
+    agent_cfg["params"]["config"]["max_epochs"] = (
+        args_cli.max_iterations if args_cli.max_iterations is not None else agent_cfg["params"]["config"]["max_epochs"]
+    )
+    if args_cli.checkpoint is not None:
+        resume_path = retrieve_file_path(args_cli.checkpoint)
+        agent_cfg["params"]["load_checkpoint"] = True
+        agent_cfg["params"]["load_path"] = resume_path
+        print(f"[INFO]: Loading model checkpoint from: {agent_cfg['params']['load_path']}")
+    train_sigma = float(args_cli.sigma) if args_cli.sigma is not None else None
+
+    # multi-gpu training config
+    if args_cli.distributed:
+        agent_cfg["params"]["seed"] += app_launcher.global_rank
+        agent_cfg["params"]["config"]["device"] = f"cuda:{app_launcher.local_rank}"
+        agent_cfg["params"]["config"]["device_name"] = f"cuda:{app_launcher.local_rank}"
+        agent_cfg["params"]["config"]["multi_gpu"] = True
+        # update env config device
+        env_cfg.sim.device = f"cuda:{app_launcher.local_rank}"
+
+    # set the environment seed (after multi-gpu config for updated rank from agent seed)
+    # note: certain randomizations occur in the environment initialization so we set the seed here
+    env_cfg.seed = agent_cfg["params"]["seed"]
+
+    # specify directory for logging experiments
+    log_root_path = os.path.join("logs", "rl_games", agent_cfg["params"]["config"]["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
+    log_dir = agent_cfg["params"]["config"].get("full_experiment_name", datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))
+    # set directory into agent config
+    # logging directory path: <train_dir>/<full_experiment_name>
+    agent_cfg["params"]["config"]["train_dir"] = log_root_path
+    agent_cfg["params"]["config"]["full_experiment_name"] = log_dir
+
+    # dump the configuration into log-directory
+    dump_yaml(os.path.join(log_root_path, log_dir, "params", "env.yaml"), env_cfg)
+    dump_yaml(os.path.join(log_root_path, log_dir, "params", "agent.yaml"), agent_cfg)
+    dump_pickle(os.path.join(log_root_path, log_dir, "params", "env.pkl"), env_cfg)
+    dump_pickle(os.path.join(log_root_path, log_dir, "params", "agent.pkl"), agent_cfg)
+
+    # read configurations about the agent-training
+    rl_device = agent_cfg["params"]["config"]["device"]
+    clip_obs = agent_cfg["params"]["env"].get("clip_observations", math.inf)
+    clip_actions = agent_cfg["params"]["env"].get("clip_actions", math.inf)
+
+    # 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, DirectMARLEnv):
+        env = multi_agent_to_single_agent(env)
+
+    # wrap for video recording
+    if args_cli.video:
+        video_kwargs = {
+            "video_folder": os.path.join(log_root_path, 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)
+
+    # wrap around environment for rl-games
+    env = RlGamesVecEnvWrapper(env, rl_device, clip_obs, clip_actions)
+
+    # register the environment to rl-games registry
+    # note: in agents configuration: environment name must be "rlgpu"
+    vecenv.register(
+        "IsaacRlgWrapper", lambda config_name, num_actors, **kwargs: RlGamesGpuEnv(config_name, num_actors, **kwargs)
+    )
+    env_configurations.register("rlgpu", {"vecenv_type": "IsaacRlgWrapper", "env_creator": lambda **kwargs: env})
+
+    # set number of actors into agent config
+    agent_cfg["params"]["config"]["num_actors"] = env.unwrapped.num_envs
+    # create runner from rl-games
+    runner = Runner(IsaacAlgoObserver())
+    runner.load(agent_cfg)
+
+    # reset the agent and env
+    runner.reset()
+    # train the agent
+    if args_cli.checkpoint is not None:
+        runner.run({"train": True, "play": False, "sigma": train_sigma, "checkpoint": resume_path})
+    else:
+        runner.run({"train": True, "play": False, "sigma": train_sigma})
+
+    # close the simulator
+    env.close()
+
+
+if __name__ == "__main__":
+    # run the main function
+    main()
+    # close sim app
+    simulation_app.close()