initial commit

scripts/rl_games/play.py

@@ -0,0 +1,207 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Script to play a checkpoint if an RL agent from RL-Games."""
+
+"""Launch Isaac Sim Simulator first."""
+
+import argparse
+
+from isaaclab.app import AppLauncher
+
+# add argparse arguments
+parser = argparse.ArgumentParser(description="Play a checkpoint of an RL agent from 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(
+    "--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("--checkpoint", type=str, default=None, help="Path to model checkpoint.")
+parser.add_argument(
+    "--use_pretrained_checkpoint",
+    action="store_true",
+    help="Use the pre-trained checkpoint from Nucleus.",
+)
+parser.add_argument(
+    "--use_last_checkpoint",
+    action="store_true",
+    help="When no checkpoint provided, use the last saved model. Otherwise use the best saved model.",
+)
+parser.add_argument("--real-time", action="store_true", default=False, help="Run in real-time, if possible.")
+# append AppLauncher cli args
+AppLauncher.add_app_launcher_args(parser)
+# parse the arguments
+args_cli = parser.parse_args()
+# always enable cameras to record video
+if args_cli.video:
+    args_cli.enable_cameras = True
+
+# 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 time
+import torch
+
+from rl_games.common import env_configurations, vecenv
+from rl_games.common.player import BasePlayer
+from rl_games.torch_runner import Runner
+
+from isaaclab.envs import DirectMARLEnv, multi_agent_to_single_agent
+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.rl_games import RlGamesGpuEnv, RlGamesVecEnvWrapper
+
+import isaaclab_tasks  # noqa: F401
+from isaaclab_tasks.utils import get_checkpoint_path, load_cfg_from_registry, parse_env_cfg
+
+import FLEXR_v0.tasks  # noqa: F401
+
+
+def main():
+    """Play with RL-Games agent."""
+    # parse env configuration
+    env_cfg = parse_env_cfg(
+        args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs, use_fabric=not args_cli.disable_fabric
+    )
+    agent_cfg = load_cfg_from_registry(args_cli.task, "rl_games_cfg_entry_point")
+
+    # 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] Loading experiment from directory: {log_root_path}")
+    # find checkpoint
+    if args_cli.use_pretrained_checkpoint:
+        resume_path = get_published_pretrained_checkpoint("rl_games", args_cli.task)
+        if not resume_path:
+            print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.")
+            return
+    elif args_cli.checkpoint is None:
+        # specify directory for logging runs
+        run_dir = agent_cfg["params"]["config"].get("full_experiment_name", ".*")
+        # specify name of checkpoint
+        if args_cli.use_last_checkpoint:
+            checkpoint_file = ".*"
+        else:
+            # this loads the best checkpoint
+            checkpoint_file = f"{agent_cfg['params']['config']['name']}.pth"
+        # get path to previous checkpoint
+        resume_path = get_checkpoint_path(log_root_path, run_dir, checkpoint_file, other_dirs=["nn"])
+    else:
+        resume_path = retrieve_file_path(args_cli.checkpoint)
+    log_dir = os.path.dirname(os.path.dirname(resume_path))
+
+    # wrap around environment for rl-games
+    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", "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 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})
+
+    # load previously trained model
+    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']}")
+
+    # set number of actors into agent config
+    agent_cfg["params"]["config"]["num_actors"] = env.unwrapped.num_envs
+    # create runner from rl-games
+    runner = Runner()
+    runner.load(agent_cfg)
+    # obtain the agent from the runner
+    agent: BasePlayer = runner.create_player()
+    agent.restore(resume_path)
+    agent.reset()
+
+    dt = env.unwrapped.step_dt
+
+    # reset environment
+    obs = env.reset()
+    if isinstance(obs, dict):
+        obs = obs["obs"]
+    timestep = 0
+    # required: enables the flag for batched observations
+    _ = agent.get_batch_size(obs, 1)
+    # initialize RNN states if used
+    if agent.is_rnn:
+        agent.init_rnn()
+    # simulate environment
+    # note: We simplified the logic in rl-games player.py (:func:`BasePlayer.run()`) function in an
+    #   attempt to have complete control over environment stepping. However, this removes other
+    #   operations such as masking that is used for multi-agent learning by RL-Games.
+    while simulation_app.is_running():
+        start_time = time.time()
+        # run everything in inference mode
+        with torch.inference_mode():
+            # convert obs to agent format
+            obs = agent.obs_to_torch(obs)
+            # agent stepping
+            actions = agent.get_action(obs, is_deterministic=agent.is_deterministic)
+            # env stepping
+            obs, _, dones, _ = env.step(actions)
+
+            # perform operations for terminated episodes
+            if len(dones) > 0:
+                # reset rnn state for terminated episodes
+                if agent.is_rnn and agent.states is not None:
+                    for s in agent.states:
+                        s[:, dones, :] = 0.0
+        if args_cli.video:
+            timestep += 1
+            # Exit the play loop after recording one video
+            if timestep == args_cli.video_length:
+                break
+
+        # time delay for real-time evaluation
+        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()
+
+
+if __name__ == "__main__":
+    # run the main function
+    main()
+    # close sim app
+    simulation_app.close()