215 lines
8.5 KiB
Python
215 lines
8.5 KiB
Python
# Copyright (c) 2025 Deep Robotics
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# SPDX-License-Identifier: BSD 3-Clause
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# Copyright (c) 2024-2025 Ziqi Fan
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# SPDX-License-Identifier: Apache-2.0
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# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
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# All rights reserved.
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#
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# SPDX-License-Identifier: Apache-2.0
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"""Script to train RL agent with RSL-RL."""
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"""Launch Isaac Sim Simulator first."""
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import argparse
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import os
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import sys
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PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
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PROJECT_SOURCE_PATH = os.path.join(PROJECT_ROOT, "source", "rl_training")
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if PROJECT_SOURCE_PATH not in sys.path:
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sys.path.insert(0, PROJECT_SOURCE_PATH)
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from isaaclab.app import AppLauncher
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# local imports
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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import cli_args
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# add argparse arguments
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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("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).")
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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("--max_iterations", type=int, default=None, help="RL Policy training iterations.")
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parser.add_argument(
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"--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes."
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)
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# append RSL-RL cli arguments
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cli_args.add_rsl_rl_args(parser)
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# append AppLauncher cli args
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AppLauncher.add_app_launcher_args(parser)
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args_cli, hydra_args = parser.parse_known_args()
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# always enable cameras to record video
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if args_cli.video:
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args_cli.enable_cameras = True
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# clear out sys.argv for Hydra
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sys.argv = [sys.argv[0]] + hydra_args
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# launch omniverse app
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app_launcher = AppLauncher(args_cli)
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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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import os
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import torch
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from datetime import datetime
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from rsl_rl.runners import OnPolicyRunner
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from isaaclab.envs import (
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DirectMARLEnv,
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DirectMARLEnvCfg,
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DirectRLEnvCfg,
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ManagerBasedRLEnvCfg,
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multi_agent_to_single_agent,
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)
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from isaaclab.utils.dict import print_dict
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from isaaclab.utils.io import dump_yaml
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from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlVecEnvWrapper
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from isaaclab_tasks.utils import get_checkpoint_path
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from isaaclab_tasks.utils.hydra import hydra_task_config
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import rl_training
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import rl_training.tasks # noqa: F401
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print(f"[INFO] Using rl_training package from: {rl_training.__file__}")
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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torch.backends.cudnn.deterministic = False
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torch.backends.cudnn.benchmark = False
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def resolve_resume_path(log_root_path: str, load_run: str | None, load_checkpoint: str | None) -> str:
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"""Resolve a resume checkpoint, allowing explicit paths across experiment folders."""
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if load_checkpoint is not None:
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checkpoint_path = os.path.expanduser(load_checkpoint)
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if os.path.isabs(checkpoint_path) or os.path.dirname(checkpoint_path):
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checkpoint_path = os.path.abspath(checkpoint_path)
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if not os.path.isfile(checkpoint_path):
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raise FileNotFoundError(f"Unable to find the checkpoint file: {checkpoint_path}")
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return checkpoint_path
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if load_run is not None:
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run_path = os.path.expanduser(load_run)
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if os.path.isabs(run_path) or os.path.dirname(run_path):
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run_path = os.path.abspath(run_path)
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if not os.path.isdir(run_path):
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raise FileNotFoundError(f"Unable to find the run directory: {run_path}")
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return get_checkpoint_path(os.path.dirname(run_path), os.path.basename(run_path), load_checkpoint)
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return get_checkpoint_path(log_root_path, load_run, load_checkpoint)
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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: RslRlOnPolicyRunnerCfg):
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"""Train with RSL-RL agent."""
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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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agent_cfg.max_iterations = (
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args_cli.max_iterations if args_cli.max_iterations is not None else agent_cfg.max_iterations
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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
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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
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for reward_name in (
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"track_diff_wheel_yaw_exp",
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"lin_vel_xy_yaw_command_l2",
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"hipx_joint_pos_penalty",
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"hipy_joint_pos_penalty",
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):
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reward_term = getattr(env_cfg.rewards, reward_name, None)
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reward_weight = None if reward_term is None else reward_term.weight
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print(f"[INFO] Reward term {reward_name}: weight={reward_weight}")
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# multi-gpu training configuration
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if args_cli.distributed:
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env_cfg.sim.device = f"cuda:{app_launcher.local_rank}"
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agent_cfg.device = f"cuda:{app_launcher.local_rank}"
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# set seed to have diversity in different threads
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seed = agent_cfg.seed + app_launcher.local_rank
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env_cfg.seed = seed
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agent_cfg.seed = seed
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# specify directory for logging experiments
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log_root_path = os.path.join("logs", "rsl_rl", agent_cfg.experiment_name)
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log_root_path = os.path.abspath(log_root_path)
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print(f"[INFO] Logging experiment in directory: {log_root_path}")
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# specify directory for logging runs: {time-stamp}_{run_name}
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log_dir = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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# The Ray Tune workflow extracts experiment name using the logging line below, hence, do not change it (see PR #2346, comment-2819298849)
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print(f"Exact experiment name requested from command line: {log_dir}")
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if agent_cfg.run_name:
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log_dir += f"_{agent_cfg.run_name}"
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log_dir = os.path.join(log_root_path, log_dir)
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# create isaac environment
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env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
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# convert to single-agent instance if required by the RL algorithm
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if isinstance(env.unwrapped, DirectMARLEnv):
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env = multi_agent_to_single_agent(env)
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# save resume path before creating a new log_dir
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if agent_cfg.resume or agent_cfg.algorithm.class_name == "Distillation":
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resume_path = resolve_resume_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
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# wrap for video recording
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if args_cli.video:
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video_kwargs = {
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"video_folder": os.path.join(log_dir, "videos", "train"),
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"step_trigger": lambda step: step % args_cli.video_interval == 0,
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"video_length": args_cli.video_length,
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"disable_logger": True,
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}
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print("[INFO] Recording videos during training.")
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print_dict(video_kwargs, nesting=4)
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env = gym.wrappers.RecordVideo(env, **video_kwargs)
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# wrap around environment for rsl-rl
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env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions)
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# create runner from rsl-rl
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runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device)
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# write git state to logs
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runner.add_git_repo_to_log(__file__)
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# load the checkpoint
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if agent_cfg.resume or agent_cfg.algorithm.class_name == "Distillation":
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print(f"[INFO]: Loading model checkpoint from: {resume_path}")
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# load previously trained model
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runner.load(resume_path)
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# dump the configuration into log-directory
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dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg)
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dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg)
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# run training
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runner.learn(num_learning_iterations=agent_cfg.max_iterations, init_at_random_ep_len=True)
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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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# close sim app
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simulation_app.close()
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