208 lines
7.9 KiB
Python
208 lines
7.9 KiB
Python
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# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
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# All rights reserved.
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#
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# SPDX-License-Identifier: BSD-3-Clause
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"""Script to play a checkpoint if an RL agent from RL-Games."""
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"""Launch Isaac Sim Simulator first."""
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import argparse
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from isaaclab.app import AppLauncher
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# add argparse arguments
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parser = argparse.ArgumentParser(description="Play a checkpoint of an RL agent from RL-Games.")
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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(
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"--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
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)
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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("--checkpoint", type=str, default=None, help="Path to model checkpoint.")
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parser.add_argument(
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"--use_pretrained_checkpoint",
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action="store_true",
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help="Use the pre-trained checkpoint from Nucleus.",
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)
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parser.add_argument(
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"--use_last_checkpoint",
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action="store_true",
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help="When no checkpoint provided, use the last saved model. Otherwise use the best saved model.",
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)
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parser.add_argument("--real-time", action="store_true", default=False, help="Run in real-time, if possible.")
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# append AppLauncher cli args
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AppLauncher.add_app_launcher_args(parser)
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# parse the arguments
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args_cli = parser.parse_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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# 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 math
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import os
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import time
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import torch
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from rl_games.common import env_configurations, vecenv
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from rl_games.common.player import BasePlayer
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from rl_games.torch_runner import Runner
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from isaaclab.envs import DirectMARLEnv, multi_agent_to_single_agent
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from isaaclab.utils.assets import retrieve_file_path
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from isaaclab.utils.dict import print_dict
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from isaaclab.utils.pretrained_checkpoint import get_published_pretrained_checkpoint
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from isaaclab_rl.rl_games import RlGamesGpuEnv, RlGamesVecEnvWrapper
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import isaaclab_tasks # noqa: F401
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from isaaclab_tasks.utils import get_checkpoint_path, load_cfg_from_registry, parse_env_cfg
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import FLEXR_v0.tasks # noqa: F401
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def main():
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"""Play with RL-Games agent."""
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# parse env configuration
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env_cfg = parse_env_cfg(
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args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs, use_fabric=not args_cli.disable_fabric
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)
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agent_cfg = load_cfg_from_registry(args_cli.task, "rl_games_cfg_entry_point")
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# specify directory for logging experiments
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log_root_path = os.path.join("logs", "rl_games", agent_cfg["params"]["config"]["name"])
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log_root_path = os.path.abspath(log_root_path)
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print(f"[INFO] Loading experiment from directory: {log_root_path}")
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# find checkpoint
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if args_cli.use_pretrained_checkpoint:
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resume_path = get_published_pretrained_checkpoint("rl_games", args_cli.task)
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if not resume_path:
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print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.")
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return
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elif args_cli.checkpoint is None:
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# specify directory for logging runs
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run_dir = agent_cfg["params"]["config"].get("full_experiment_name", ".*")
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# specify name of checkpoint
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if args_cli.use_last_checkpoint:
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checkpoint_file = ".*"
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else:
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# this loads the best checkpoint
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checkpoint_file = f"{agent_cfg['params']['config']['name']}.pth"
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# get path to previous checkpoint
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resume_path = get_checkpoint_path(log_root_path, run_dir, checkpoint_file, other_dirs=["nn"])
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else:
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resume_path = retrieve_file_path(args_cli.checkpoint)
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log_dir = os.path.dirname(os.path.dirname(resume_path))
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# wrap around environment for rl-games
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rl_device = agent_cfg["params"]["config"]["device"]
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clip_obs = agent_cfg["params"]["env"].get("clip_observations", math.inf)
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clip_actions = agent_cfg["params"]["env"].get("clip_actions", math.inf)
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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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# 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_root_path, log_dir, "videos", "play"),
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"step_trigger": lambda step: step == 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 rl-games
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env = RlGamesVecEnvWrapper(env, rl_device, clip_obs, clip_actions)
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# register the environment to rl-games registry
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# note: in agents configuration: environment name must be "rlgpu"
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vecenv.register(
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"IsaacRlgWrapper", lambda config_name, num_actors, **kwargs: RlGamesGpuEnv(config_name, num_actors, **kwargs)
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)
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env_configurations.register("rlgpu", {"vecenv_type": "IsaacRlgWrapper", "env_creator": lambda **kwargs: env})
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# load previously trained model
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agent_cfg["params"]["load_checkpoint"] = True
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agent_cfg["params"]["load_path"] = resume_path
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print(f"[INFO]: Loading model checkpoint from: {agent_cfg['params']['load_path']}")
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# set number of actors into agent config
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agent_cfg["params"]["config"]["num_actors"] = env.unwrapped.num_envs
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# create runner from rl-games
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runner = Runner()
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runner.load(agent_cfg)
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# obtain the agent from the runner
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agent: BasePlayer = runner.create_player()
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agent.restore(resume_path)
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agent.reset()
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dt = env.unwrapped.step_dt
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# reset environment
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obs = env.reset()
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if isinstance(obs, dict):
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obs = obs["obs"]
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timestep = 0
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# required: enables the flag for batched observations
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_ = agent.get_batch_size(obs, 1)
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# initialize RNN states if used
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if agent.is_rnn:
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agent.init_rnn()
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# simulate environment
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# note: We simplified the logic in rl-games player.py (:func:`BasePlayer.run()`) function in an
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# attempt to have complete control over environment stepping. However, this removes other
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# operations such as masking that is used for multi-agent learning by RL-Games.
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while simulation_app.is_running():
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start_time = time.time()
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# run everything in inference mode
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with torch.inference_mode():
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# convert obs to agent format
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obs = agent.obs_to_torch(obs)
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# agent stepping
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actions = agent.get_action(obs, is_deterministic=agent.is_deterministic)
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# env stepping
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obs, _, dones, _ = env.step(actions)
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# perform operations for terminated episodes
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if len(dones) > 0:
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# reset rnn state for terminated episodes
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if agent.is_rnn and agent.states is not None:
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for s in agent.states:
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s[:, dones, :] = 0.0
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if args_cli.video:
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timestep += 1
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# Exit the play loop after recording one video
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if timestep == args_cli.video_length:
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break
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# time delay for real-time evaluation
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sleep_time = dt - (time.time() - start_time)
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if args_cli.real_time and sleep_time > 0:
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time.sleep(sleep_time)
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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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