feat(simulate): metadata config loading
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5 changed files with 190 additions and 476 deletions
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@ -1,11 +1,10 @@
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"""Simulate a trained policy in the MuJoCo viewer.
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Uses Hydra to load the same BrittleStarConfig that was used during training.
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Override settings via CLI, e.g.:
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python scripts/simulate.py morphology=3_arms
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To replay a run using the *exact* Hydra config used during training, pass:
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python scripts/simulate.py simulation.trained_config_path=runs/.../.hydra/config.yaml \
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Automatically extracts the training configuration (morphology, environment, etc.)
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from the sidecar metadata YAML file to ensure simulation perfectly matches training.
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Override simulation settings via CLI, e.g.:
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uv run scripts/simulate.py \
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simulation.morphology_override=config/morphology/3_arms.yaml \
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simulation.model_path=runs/.../final_model.flax
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"""
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@ -22,85 +21,24 @@ import jax
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import jax.numpy as jnp
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import numpy as np
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import yaml
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from omegaconf import DictConfig, OmegaConf, open_dict
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from omegaconf import DictConfig, OmegaConf
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from brittle_star_project import Backend, BrittleStarEnv, BrittleStarEnvFactory
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from brittle_star_project.configs.main_config import BrittleStarConfig
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from brittle_star_project.configs.register_configs import register_configs
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from brittle_star_project.environment.padded_obs_wrapper import compute_padding_masks
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from brittle_star_project.environment.obs_processing import create_obs_processor
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from brittle_star_project.environment.env_config import ObservationBoundsConfig
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from brittle_star_project.environment.env_config import (
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MorphologyConfig,
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ArenaConfig,
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EnvConfig,
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ObservationBoundsConfig,
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)
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def _dense_layer_sizes_from_params(params: Any) -> list[int]:
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"""Infer GenericDenseLayersWithActivation.layer_sizes from a Flax params tree."""
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class PolicyAgent:
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"""Wraps a trained Flax actor for deterministic inference."""
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try:
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dense_params = params["params"]
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except Exception as exc:
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raise ValueError("Unexpected sensor params structure (missing 'params')") from exc
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layer_sizes: list[int] = []
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idx = 0
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while True:
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key = f"Dense_{idx}"
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if key not in dense_params:
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break
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kernel = dense_params[key]["kernel"]
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layer_sizes.append(int(np.asarray(kernel).shape[1]))
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idx += 1
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if not layer_sizes:
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raise ValueError("Could not infer Dense_* layers from sensor params")
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return layer_sizes
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def _infer_action_dim_from_actor_params(params: Any) -> int | None:
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"""Best-effort infer action_dim from a Flax Actor params tree."""
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try:
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dense0 = params["params"]["Dense_0"]
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bias = dense0.get("bias")
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kernel = dense0.get("kernel")
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except Exception:
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return None
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if bias is not None:
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try:
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return int(np.asarray(bias).shape[0])
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except Exception:
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return None
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if kernel is not None:
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try:
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return int(np.asarray(kernel).shape[1])
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except Exception:
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return None
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return None
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def _has_cli_override(overrides: list[str], key: str) -> bool:
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prefixes = (f"{key}=", f"{key}.", f"+{key}=", f"+{key}.")
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return any(str(o).startswith(prefixes) for o in overrides)
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def _maybe_clip_action(
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action: np.ndarray,
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low: np.ndarray | None,
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high: np.ndarray | None,
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) -> np.ndarray:
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if low is None or high is None:
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return action
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low = np.asarray(low, dtype=np.float32).ravel()
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high = np.asarray(high, dtype=np.float32).ravel()
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if low.shape != action.shape or high.shape != action.shape:
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return action
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return np.clip(action, low, high)
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# A minimal policy class to load a CleanRL/Flax checkpoint and run inference.
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class CleanRLPPOPolicy:
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def __init__(
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self,
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*,
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@ -111,7 +49,28 @@ class CleanRLPPOPolicy:
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) -> None:
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from brittle_star_project.MLPs.mlps import Actor, GenericDenseLayersWithActivation
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layer_sizes = _dense_layer_sizes_from_params(sensor_params)
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# Infer layer sizes from params
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try:
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dense_params = (
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sensor_params.get("params", {})
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if isinstance(sensor_params, dict)
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else sensor_params["params"]
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)
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except Exception:
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dense_params = sensor_params
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layer_sizes = []
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idx = 0
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while True:
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key = f"Dense_{idx}"
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if key not in dense_params:
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break
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layer_sizes.append(int(np.asarray(dense_params[key]["kernel"]).shape[1]))
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idx += 1
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if not layer_sizes:
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raise ValueError("Could not infer Dense_* layers from sensor params")
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self._sensor = GenericDenseLayersWithActivation(layer_sizes=layer_sizes)
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self._actor = Actor(action_dim=action_dim)
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self._sensor_apply = jax.jit(self._sensor.apply)
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@ -128,122 +87,31 @@ class CleanRLPPOPolicy:
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*,
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action_dim: int,
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obs_processor: Any,
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) -> "CleanRLPPOPolicy":
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def _get_index(container: Any, idx: int) -> Any:
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if isinstance(container, (list, tuple)):
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return container[idx]
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if isinstance(container, dict):
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return container.get(idx, container.get(str(idx)))
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raise KeyError(idx)
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def _looks_like_indexed_dict(container: Any) -> bool:
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return (
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isinstance(container, dict)
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and container
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and all(str(k).isdigit() for k in container.keys())
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)
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def _parse_checkpoint(restored_obj: Any) -> tuple[Any, Any, Any, Any, Any]:
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"""Extract checkpoint parts.
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Returns (config_dict, sensor_params, actor_params, critic_params,
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feature_extractor_params).
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PPOTrainer saves:
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flax.serialization.to_bytes([
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config_dict,
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[sensor_params, actor_params, critic_params, feature_extractor_params],
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])
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msgpack_restore() may restore lists as dicts keyed by string indices
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("0", "1", ...), so we accept both shapes.
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"""
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cfg_part: Any | None = None
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params_part: Any = restored_obj
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if isinstance(restored_obj, (list, tuple)) and len(restored_obj) >= 2:
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cfg_part = restored_obj[0]
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params_part = restored_obj[1]
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elif _looks_like_indexed_dict(restored_obj) and (
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"0" in restored_obj or "1" in restored_obj
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):
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cfg_part = restored_obj.get("0", restored_obj.get(0))
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params_part = restored_obj.get("1", restored_obj.get(1))
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if _looks_like_indexed_dict(params_part):
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sensor_params = _get_index(params_part, 0)
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actor_params = _get_index(params_part, 1)
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critic_params = _get_index(params_part, 2)
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feature_extractor_params = _get_index(params_part, 3)
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if sensor_params is None or actor_params is None:
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raise ValueError("Missing required params in checkpoint")
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return (
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cfg_part,
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sensor_params,
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actor_params,
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critic_params,
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feature_extractor_params,
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)
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if isinstance(params_part, (list, tuple)) and len(params_part) >= 2:
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sensor_params = params_part[0]
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actor_params = params_part[1]
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critic_params = params_part[2] if len(params_part) >= 3 else None
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feature_extractor_params = params_part[3] if len(params_part) >= 4 else None
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return (
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cfg_part,
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sensor_params,
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actor_params,
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critic_params,
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feature_extractor_params,
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)
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# Accept a plain dict-shaped Flax params mapping commonly produced
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# by saving `agent_state.params` directly. Typical keys are
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# 'sensor_params' and 'actor_params', or sometimes nested under 'params'.
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if isinstance(restored_obj, dict):
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# Top-level params dict
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params_sub = restored_obj.get("params", {})
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sensor_params = restored_obj.get("sensor_params") or params_sub.get("sensor_params")
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actor_params = restored_obj.get("actor_params") or params_sub.get("actor_params")
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critic_params = restored_obj.get("critic_params") or params_sub.get("critic_params")
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feature_extractor_params = restored_obj.get(
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"feature_extractor_params"
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) or params_sub.get("feature_extractor_params")
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# Some checkpoints only save actor+sensor as top-level
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if sensor_params is not None and actor_params is not None:
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return (
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cfg_part,
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sensor_params,
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actor_params,
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critic_params,
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feature_extractor_params,
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)
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raise ValueError(
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f"Unexpected checkpoint structure in {path}. "
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"Expected [config_dict, [sensor_params, actor_params, critic_params, "
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"feature_extractor_params]] or an equivalent dict-indexed variant."
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)
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) -> "PolicyAgent":
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payload = path.read_bytes()
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restored = flax.serialization.msgpack_restore(payload)
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_cfg_dict, sensor_params, actor_params, _critic_params, _feature_extractor_params = (
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_parse_checkpoint(restored)
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)
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ckpt_action_dim = _infer_action_dim_from_actor_params(actor_params)
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if ckpt_action_dim is not None and ckpt_action_dim != action_dim:
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raise ValueError(
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"Checkpoint/env mismatch: "
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f"checkpoint expects action_dim={ckpt_action_dim}, "
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f"env provides action_dim={action_dim}. "
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"Use the same Hydra config (morphology/arena/environment) "
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"that was used during training."
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)
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sensor_params = None
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actor_params = None
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return CleanRLPPOPolicy(
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# Extract params from restored checkpoint
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if isinstance(restored, dict):
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params_sub = restored.get("params", {})
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sensor_params = restored.get("sensor_params") or params_sub.get("sensor_params")
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actor_params = restored.get("actor_params") or params_sub.get("actor_params")
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elif isinstance(restored, (list, tuple)) and len(restored) >= 2:
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params_part = restored[1]
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if isinstance(params_part, dict):
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sensor_params = params_part.get("0", params_part.get(0))
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actor_params = params_part.get("1", params_part.get(1))
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elif isinstance(params_part, (list, tuple)) and len(params_part) >= 2:
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sensor_params = params_part[0]
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actor_params = params_part[1]
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if sensor_params is None or actor_params is None:
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raise ValueError(f"Could not extract sensor and actor params from checkpoint: {path}")
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return PolicyAgent(
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sensor_params=sensor_params,
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actor_params=actor_params,
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action_dim=action_dim,
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@ -273,23 +141,30 @@ def _target_reached(*, state: Any) -> bool:
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return bool(getattr(state, "terminated", False) or getattr(state, "truncated", False))
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def _rollout_one_episode_headless(
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def _maybe_clip_action(
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action: np.ndarray,
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low: np.ndarray | None,
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high: np.ndarray | None,
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) -> np.ndarray:
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if low is None or high is None:
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return action
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low = np.asarray(low, dtype=np.float32).ravel()
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high = np.asarray(high, dtype=np.float32).ravel()
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if low.shape != action.shape or high.shape != action.shape:
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return action
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return np.clip(action, low, high)
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def _rollout_headless(
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*,
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env: BrittleStarEnv,
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policy: CleanRLPPOPolicy,
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policy: PolicyAgent,
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seed: int,
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max_steps: int,
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action_low: np.ndarray | None,
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action_high: np.ndarray | None,
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action_mask: np.ndarray | None = None,
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) -> tuple[float, int, bool, float | None]:
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"""Run one rollout up to max_steps.
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Returns (return, length, reached_target, final_xy_dist).
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Note: In the MJC backend, the raw env reward can be 0.0; we compute a simple
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progress reward based on xy_distance_to_target.
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"""
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state = env.reset(seed=seed)
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ep_return = 0.0
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@ -302,12 +177,10 @@ def _rollout_one_episode_headless(
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obs_dict = observations or {}
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action = policy.act(observations=obs_dict)
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if action_mask is not None:
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action = action[action_mask]
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action = _maybe_clip_action(action, action_low, action_high)
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nu = int(state.mj_model.nu)
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if nu > 0 and action.shape != (nu,):
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raise ValueError(f"Policy returned action shape {action.shape}, expected ({nu},)")
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state = env.step(state=state, action=action)
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steps += 1
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@ -325,23 +198,23 @@ def _rollout_one_episode_headless(
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return ep_return, steps, reached_target, final_dist
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def _run_one_episode_viewer(
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def _rollout_viewer(
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*,
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env: BrittleStarEnv,
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policy: CleanRLPPOPolicy,
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policy: PolicyAgent,
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seed: int,
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state: Any,
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control_dt: float,
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max_steps: int | None,
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action_low: np.ndarray | None,
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action_high: np.ndarray | None,
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action_mask: np.ndarray | None = None,
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) -> None:
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import mujoco.viewer
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model = state.mj_model
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data = state.mj_data
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_ = int(seed)
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episode_return = 0.0
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observations = _get_observations(state)
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prev_dist = _get_xy_distance_to_target(observations)
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@ -359,11 +232,9 @@ def _run_one_episode_viewer(
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obs_dict = observations or {}
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action = policy.act(observations=obs_dict)
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if action_mask is not None:
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action = action[action_mask]
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action = _maybe_clip_action(action, action_low, action_high)
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if model.nu > 0 and action.shape != (int(model.nu),):
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raise ValueError(
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f"Policy returned action shape {action.shape}, expected ({int(model.nu)},)"
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)
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# The passive viewer runs a GUI thread; protect MuJoCo state mutation.
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with viewer.lock():
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@ -398,199 +269,117 @@ def _run_one_episode_viewer(
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)
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def _infer_checkpoint_obs_dim(policy: CleanRLPPOPolicy) -> int | None:
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"""Best-effort read of the first Dense kernel input dim (obs dim)."""
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try:
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kernel = policy._params["sensor_params"]["params"]["Dense_0"]["kernel"]
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return int(getattr(kernel, "shape")[0])
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except Exception:
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return None
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def _load_trained_config(path: Path) -> DictConfig:
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"""Load a trained config YAML.
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Supports both:
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- Hydra's run config (e.g. runs/.../.hydra/config.yaml)
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- This project's logger metadata YAMLs, which may contain
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``!!python/object/apply:...`` tags for Enums.
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For safety, we *do not* execute Python constructors from YAML; we only
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treat these tags as data and extract their scalar arguments.
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"""
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if not path.exists():
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raise FileNotFoundError(f"trained_config_path does not exist: '{path}'.")
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if not path.is_file():
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raise ValueError(f"trained_config_path must be a file, got: '{path}'.")
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try:
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return OmegaConf.load(path)
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except Exception as exc:
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python_apply_prefix = "tag:yaml.org,2002:python/object/apply:"
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class _SafeLoaderWithPythonApply(yaml.SafeLoader):
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pass
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def _construct_python_apply(
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loader: yaml.SafeLoader,
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_tag_suffix: str,
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node: yaml.Node,
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) -> Any:
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if isinstance(node, yaml.SequenceNode):
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seq = loader.construct_sequence(node)
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if len(seq) == 1:
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return seq[0]
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return seq
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if isinstance(node, yaml.MappingNode):
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return loader.construct_mapping(node)
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return loader.construct_scalar(node)
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_SafeLoaderWithPythonApply.add_multi_constructor(
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python_apply_prefix, _construct_python_apply
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def _load_metadata_yaml(model_path: Path) -> dict:
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"""Discover and load the sidecar metadata YAML file."""
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metadata_path = model_path.with_name(model_path.stem + "_metadata.yaml")
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if not metadata_path.exists():
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raise FileNotFoundError(
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f"Could not find metadata YAML for {model_path.name}. Expected it at {metadata_path}"
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)
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try:
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data = yaml.load(path.read_text(encoding="utf-8"), Loader=_SafeLoaderWithPythonApply)
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except Exception as yaml_exc:
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raise ValueError(
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"Failed to load trained_config_path as YAML. "
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"If this is a Hydra run, pass the run's '.hydra/config.yaml' file. "
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f"Got: '{path}'."
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) from yaml_exc
|
||||
|
||||
if not isinstance(data, dict):
|
||||
raise ValueError(
|
||||
"trained_config_path must contain a YAML mapping (dict-like) at the root. "
|
||||
f"Got type={type(data).__name__} from '{path}'."
|
||||
) from exc
|
||||
|
||||
# Normalize known enum-like strings to their Enum *names* so OmegaConf's
|
||||
# structured config merge behaves like the normal Hydra config.
|
||||
from brittle_star_project.environment.env_types import Task
|
||||
|
||||
env_cfg = data.get("environment")
|
||||
if isinstance(env_cfg, dict) and isinstance(env_cfg.get("task"), str):
|
||||
task_str = str(env_cfg["task"])
|
||||
try:
|
||||
env_cfg["task"] = Task[task_str].name
|
||||
except Exception:
|
||||
try:
|
||||
env_cfg["task"] = Task(task_str).name
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return OmegaConf.create(data)
|
||||
with open(metadata_path, "r") as f:
|
||||
return yaml.safe_load(f)
|
||||
|
||||
|
||||
@hydra.main(config_path="../configs", config_name="main_config", version_base="1.3")
|
||||
def main(dict_cfg: DictConfig) -> None:
|
||||
# Compose against the structured schema first, so missing keys are validated.
|
||||
cfg = OmegaConf.merge(OmegaConf.structured(BrittleStarConfig), dict_cfg)
|
||||
# 1. Hydra composes ONLY SimulationSettings
|
||||
cfg = OmegaConf.to_object(OmegaConf.merge(OmegaConf.structured(BrittleStarConfig), dict_cfg))
|
||||
sim_cfg = cfg.simulation
|
||||
|
||||
# Optional: override env-defining sections (morphology/arena/environment/architecture)
|
||||
# using the exact Hydra config that was used for training.
|
||||
trained_cfg_path = cfg.simulation.trained_config_path
|
||||
if trained_cfg_path:
|
||||
overrides_raw = OmegaConf.select(cfg, "hydra.overrides.task") or []
|
||||
overrides = [str(o) for o in overrides_raw]
|
||||
|
||||
trained_cfg_path_abs = Path(hydra.utils.to_absolute_path(trained_cfg_path))
|
||||
trained_cfg = _load_trained_config(trained_cfg_path_abs)
|
||||
if "hydra" in trained_cfg:
|
||||
with open_dict(trained_cfg):
|
||||
del trained_cfg["hydra"]
|
||||
|
||||
with open_dict(cfg):
|
||||
for key in ("morphology", "arena", "environment", "architecture"):
|
||||
if key in trained_cfg and not _has_cli_override(overrides, key):
|
||||
base_node = OmegaConf.select(cfg, key)
|
||||
override_node = OmegaConf.select(trained_cfg, key)
|
||||
try:
|
||||
cfg[key] = OmegaConf.merge(base_node, override_node)
|
||||
except Exception as exc:
|
||||
raise ValueError(
|
||||
"Failed to merge trained config into the active Hydra config. "
|
||||
f"Key={key!r}, trained_config_path='{trained_cfg_path_abs}'."
|
||||
) from exc
|
||||
|
||||
# Convert DictConfig to structured dataclass.
|
||||
config: BrittleStarConfig = OmegaConf.to_object(cfg)
|
||||
|
||||
backend = Backend.MJC
|
||||
seed = int(config.experiment.seed)
|
||||
|
||||
if getattr(config.architecture, "name", None) != "centralized":
|
||||
raise ValueError(
|
||||
"simulate.py currently only supports architecture=centralized. "
|
||||
f"Got architecture.name={getattr(config.architecture, 'name', None)!r}. "
|
||||
"(Training supports decentralized, but simulation wiring for it isn't implemented.)"
|
||||
)
|
||||
|
||||
model_path_str = config.simulation.model_path
|
||||
model_path_str = sim_cfg.model_path
|
||||
if model_path_str is None:
|
||||
raise ValueError(
|
||||
"simulation.model_path must be set to a .flax checkpoint (e.g. final_model.flax)"
|
||||
)
|
||||
|
||||
# Hydra chdir changes CWD; resolve relative paths relative to the invocation.
|
||||
model_path = Path(hydra.utils.to_absolute_path(model_path_str))
|
||||
if model_path.suffix != ".flax":
|
||||
raise ValueError(f"Expected a '.flax' checkpoint, got '{model_path.name}'.")
|
||||
|
||||
# ======= ENVIRONMENT SETUP =======
|
||||
# 2. Discover + load sidecar metadata YAML
|
||||
metadata = _load_metadata_yaml(model_path)
|
||||
|
||||
# 3. Reconstruct typed configs from metadata
|
||||
trained_morphology = OmegaConf.to_object(
|
||||
OmegaConf.merge(OmegaConf.structured(MorphologyConfig), metadata.get("morphology", {}))
|
||||
)
|
||||
trained_arena = OmegaConf.to_object(
|
||||
OmegaConf.merge(OmegaConf.structured(ArenaConfig), metadata.get("arena", {}))
|
||||
)
|
||||
|
||||
env_dict = metadata.get("environment", {})
|
||||
if isinstance(env_dict.get("task"), str):
|
||||
from brittle_star_project.environment.env_types import Task
|
||||
|
||||
try:
|
||||
env_dict["task"] = Task[env_dict["task"]].name
|
||||
except Exception:
|
||||
try:
|
||||
env_dict["task"] = Task(env_dict["task"]).name
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
trained_environment = OmegaConf.to_object(
|
||||
OmegaConf.merge(OmegaConf.structured(EnvConfig), env_dict)
|
||||
)
|
||||
trained_obs_bounds = OmegaConf.to_object(
|
||||
OmegaConf.merge(
|
||||
OmegaConf.structured(ObservationBoundsConfig), metadata.get("obs_bounds", {})
|
||||
)
|
||||
)
|
||||
|
||||
# 4. Determine environment morphology
|
||||
if sim_cfg.morphology_override is not None:
|
||||
override_path = Path(hydra.utils.to_absolute_path(sim_cfg.morphology_override))
|
||||
if not override_path.exists():
|
||||
raise FileNotFoundError(f"Could not find morphology override YAML at {override_path}")
|
||||
with open(override_path, "r") as f:
|
||||
override_dict = yaml.safe_load(f)
|
||||
env_morphology = OmegaConf.to_object(
|
||||
OmegaConf.merge(OmegaConf.structured(MorphologyConfig), override_dict)
|
||||
)
|
||||
else:
|
||||
env_morphology = trained_morphology
|
||||
|
||||
# 5. Build obs_processor with TRAINING morphology padding masks always
|
||||
padding_masks = compute_padding_masks(
|
||||
segments_per_arm=env_morphology.segments_per_arm,
|
||||
)
|
||||
obs_processor = create_obs_processor(
|
||||
bounds_dict=trained_obs_bounds.to_bounds_dict(),
|
||||
padding_masks=padding_masks,
|
||||
)
|
||||
|
||||
# 6. Build environment
|
||||
backend = Backend.MJC
|
||||
seed = int(cfg.experiment.seed)
|
||||
|
||||
factory = BrittleStarEnvFactory()
|
||||
raw_env = factory.create_environment(
|
||||
backend,
|
||||
config.morphology,
|
||||
config.arena,
|
||||
config.environment,
|
||||
env_morphology,
|
||||
trained_arena,
|
||||
trained_environment,
|
||||
)
|
||||
env = BrittleStarEnv(
|
||||
raw_env,
|
||||
backend=backend,
|
||||
config=config.environment,
|
||||
morphology_config=config.morphology,
|
||||
config=trained_environment,
|
||||
morphology_config=env_morphology,
|
||||
)
|
||||
|
||||
state0 = env.reset(seed=seed)
|
||||
|
||||
# Match training's padded observation layout for amputated morphologies.
|
||||
padding_masks = compute_padding_masks(config.morphology.segments_per_arm)
|
||||
# Calculate the action dimension the model was trained with
|
||||
trained_action_dim = sum(trained_morphology.segments_per_arm) * 2
|
||||
|
||||
training_bounds = ObservationBoundsConfig().to_bounds_dict()
|
||||
if trained_cfg_path and "obs_bounds" in trained_cfg:
|
||||
try:
|
||||
training_bounds = OmegaConf.to_object(trained_cfg.obs_bounds).to_bounds_dict()
|
||||
except Exception:
|
||||
pass
|
||||
else:
|
||||
training_bounds = config.obs_bounds.to_bounds_dict()
|
||||
|
||||
obs_processor = create_obs_processor(
|
||||
bounds_dict=training_bounds,
|
||||
padding_masks=padding_masks,
|
||||
# 7. Load policy
|
||||
policy = PolicyAgent.load(
|
||||
model_path, action_dim=trained_action_dim, obs_processor=obs_processor
|
||||
)
|
||||
|
||||
# ======= MODEL SETUP =======
|
||||
nu = int(state0.mj_model.nu)
|
||||
policy = CleanRLPPOPolicy.load(model_path, action_dim=nu, obs_processor=obs_processor)
|
||||
|
||||
# Helpful early failure when configs don't match the checkpoint.
|
||||
observations0 = _get_observations(state0)
|
||||
obs0_dict = observations0 or {}
|
||||
batched_obs0 = jax.tree.map(lambda x: jnp.asarray(x)[None, ...], obs0_dict)
|
||||
env_obs_dim = int(obs_processor(batched_obs0).shape[1])
|
||||
ckpt_obs_dim = _infer_checkpoint_obs_dim(policy)
|
||||
|
||||
if ckpt_obs_dim is not None and ckpt_obs_dim != env_obs_dim:
|
||||
raise ValueError(
|
||||
"Checkpoint/env mismatch: "
|
||||
f"checkpoint expects obs_dim={ckpt_obs_dim}, env provides obs_dim={env_obs_dim}. "
|
||||
"Use the same Hydra config (morphology/arena/environment) "
|
||||
"that was used during training."
|
||||
)
|
||||
# Convert the JAX boolean mask to a numpy array for easy indexing
|
||||
action_mask = np.asarray(padding_masks["mask_2x"])
|
||||
|
||||
# Match training's action clipping behavior.
|
||||
action_space = getattr(raw_env, "action_space", None)
|
||||
|
|
@ -601,24 +390,26 @@ def main(dict_cfg: DictConfig) -> None:
|
|||
None if action_space is None else np.asarray(action_space.high, dtype=np.float32).ravel()
|
||||
)
|
||||
|
||||
# ======= SIMULATION =======
|
||||
headless = bool(config.simulation.headless)
|
||||
max_steps = config.simulation.max_steps
|
||||
# 8. Run simulation
|
||||
headless = bool(sim_cfg.headless)
|
||||
max_steps = sim_cfg.max_steps
|
||||
|
||||
if headless:
|
||||
if max_steps is None:
|
||||
raise ValueError("simulation.max_steps is required when simulation.headless=true")
|
||||
|
||||
max_steps_i = int(max_steps)
|
||||
if max_steps_i <= 0:
|
||||
raise ValueError("simulation.max_steps must be > 0")
|
||||
|
||||
ep_return, ep_len, reached_target, final_dist = _rollout_one_episode_headless(
|
||||
ep_return, ep_len, reached_target, final_dist = _rollout_headless(
|
||||
env=env,
|
||||
policy=policy,
|
||||
seed=seed,
|
||||
max_steps=max_steps_i,
|
||||
action_low=action_low,
|
||||
action_high=action_high,
|
||||
action_mask=action_mask,
|
||||
)
|
||||
final_dist_str = "n/a" if final_dist is None else f"{final_dist:.3f}"
|
||||
print(
|
||||
|
|
@ -627,18 +418,17 @@ def main(dict_cfg: DictConfig) -> None:
|
|||
f"target_reached={reached_target}, final_xy_dist={final_dist_str}"
|
||||
)
|
||||
else:
|
||||
max_steps_val = None
|
||||
if max_steps is not None:
|
||||
max_steps_i = int(max_steps)
|
||||
if max_steps_i <= 0:
|
||||
raise ValueError("simulation.max_steps must be > 0")
|
||||
max_steps_val: int | None = max_steps_i
|
||||
else:
|
||||
max_steps_val = None
|
||||
max_steps_val = max_steps_i
|
||||
|
||||
model_dt = float(state0.mj_model.opt.timestep)
|
||||
control_dt = model_dt * float(config.environment.num_physics_steps_per_control_step)
|
||||
control_dt = model_dt * float(trained_environment.num_physics_steps_per_control_step)
|
||||
|
||||
_run_one_episode_viewer(
|
||||
_rollout_viewer(
|
||||
env=env,
|
||||
policy=policy,
|
||||
seed=seed,
|
||||
|
|
@ -647,6 +437,7 @@ def main(dict_cfg: DictConfig) -> None:
|
|||
max_steps=max_steps_val,
|
||||
action_low=action_low,
|
||||
action_high=action_high,
|
||||
action_mask=action_mask,
|
||||
)
|
||||
|
||||
env.close()
|
||||
|
|
|
|||
Reference in a new issue