Merge pull request #21 from SELab-3-2026/simulate-results
Model Simulation
This commit is contained in:
Jona Reynaert
GitHub
commit
509ad491fb
6 changed files with 754 additions and 73 deletions
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@ -4,8 +4,12 @@
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# Path to the trained model (optional)
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model_path: null
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# Type of model to use if no path is provided (e.g., random)
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model_type: "random"
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# Script behavior
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headless: false
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# In headless mode this is required; in viewer mode null means "infinite".
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max_steps: null
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# Execution backend (MJX or BRAX)
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backend: "MJX"
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# Optional: path to the Hydra config.yaml used during training.
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# When set, scripts/simulate.py will use it to default morphology/arena/environment/architecture
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# to match training (unless you explicitly override those keys via CLI).
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trained_config_path: null
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@ -3,91 +3,676 @@
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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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simulation.model_path=runs/.../final_model.flax
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"""
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from __future__ import annotations
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import hydra
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from omegaconf import DictConfig, OmegaConf
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import itertools
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import time
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from pathlib import Path
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from typing import Any
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from brittle_star_project import (
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BrittleStarEnv,
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BrittleStarEnvFactory,
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SimulationConfig,
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simulate_policy,
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)
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import flax
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import hydra
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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 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.rl import RLModel
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from brittle_star_project.rl.base import get_rl_model_registry
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from brittle_star_project.environment.padded_obs_wrapper import (
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compute_padding_masks,
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pad_observation,
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)
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MODEL_BY_NAME = get_rl_model_registry()
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MODEL_OPTIONS = sorted(MODEL_BY_NAME)
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_ALLOWED_OBS_KEYS = {
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"joint_position",
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"joint_velocity",
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"joint_actuator_force",
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"actuator_force",
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"disk_position",
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"disk_rotation",
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"disk_linear_velocity",
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"disk_angular_velocity",
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"unit_xy_direction_to_target",
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"xy_distance_to_target",
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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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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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def _transform_obs_dict(obs_dict: dict[str, Any]) -> jnp.ndarray:
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"""Flatten the env's observation dict into a 1D vector.
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Matches training behavior:
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- only includes keys in _ALLOWED_OBS_KEYS
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- iterates keys in sorted order for stable layout
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- skips empty arrays
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"""
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parts: list[jnp.ndarray] = []
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for key in sorted(obs_dict.keys()):
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if key not in _ALLOWED_OBS_KEYS:
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continue
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arr = jnp.asarray(obs_dict[key])
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if arr.size == 0:
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continue
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parts.append(arr.reshape((-1,)))
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if not parts:
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return jnp.zeros((0,), dtype=jnp.float32)
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return jnp.concatenate(parts, axis=0)
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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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sensor_params: Any,
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actor_params: Any,
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action_dim: int,
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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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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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self._actor_apply = jax.jit(self._actor.apply)
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self._params = {
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"sensor_params": sensor_params,
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"actor_params": actor_params,
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}
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@staticmethod
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def load(
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path: Path,
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*,
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action_dim: int,
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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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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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return CleanRLPPOPolicy(
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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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)
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def act(self, *, observations: dict[str, Any]) -> np.ndarray:
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obs = _transform_obs_dict(observations)
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hidden = self._sensor_apply(self._params["sensor_params"], obs)
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mean, _log_std = self._actor_apply(self._params["actor_params"], hidden)
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# Always evaluate with the actor mean.
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# (Sampling adds exploration noise, which is useful for training but not for evaluation.)
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return np.asarray(mean, dtype=np.float32).ravel()
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def _get_observations(state: Any) -> dict[str, Any] | None:
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return getattr(state, "observations", None)
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def _get_xy_distance_to_target(observations: dict[str, Any]) -> float | None:
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return float(np.asarray(observations["xy_distance_to_target"]).reshape(-1)[0])
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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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*,
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env: BrittleStarEnv,
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policy: CleanRLPPOPolicy,
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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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padding_masks: dict[str, Any] | 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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observations = _get_observations(state)
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prev_dist = _get_xy_distance_to_target(observations)
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reached_target = _target_reached(state=state)
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steps = 0
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for _ in range(int(max_steps)):
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obs_dict = observations or {}
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if padding_masks is not None:
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obs_dict = pad_observation(obs_dict, padding_masks)
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action = policy.act(observations=obs_dict)
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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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observations = _get_observations(state)
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cur_dist = _get_xy_distance_to_target(observations)
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if prev_dist is not None and cur_dist is not None:
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ep_return += prev_dist - cur_dist
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prev_dist = cur_dist
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reached_target = _target_reached(state=state)
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if reached_target:
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break
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final_dist = _get_xy_distance_to_target(observations)
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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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*,
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env: BrittleStarEnv,
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policy: CleanRLPPOPolicy,
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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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padding_masks: dict[str, Any] | 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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reached_target = _target_reached(state=state)
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steps = 0
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# Use the viewer as a context manager to avoid GLX teardown races.
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with mujoco.viewer.launch_passive(model, data) as viewer:
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step_iter = range(int(max_steps)) if max_steps is not None else itertools.count()
|
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for _step_idx in step_iter:
|
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if not viewer.is_running():
|
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break
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step_start = time.time()
|
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|
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obs_dict = observations or {}
|
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if padding_masks is not None:
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obs_dict = pad_observation(obs_dict, padding_masks)
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|
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action = policy.act(observations=obs_dict)
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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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|
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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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state = env.step(state=state, action=action)
|
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|
||||
if not viewer.is_running():
|
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break
|
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viewer.sync()
|
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|
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steps += 1
|
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|
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observations = _get_observations(state)
|
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cur_dist = _get_xy_distance_to_target(observations)
|
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if prev_dist is not None and cur_dist is not None:
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episode_return += prev_dist - cur_dist
|
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prev_dist = cur_dist
|
||||
|
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reached_target = _target_reached(state=state)
|
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if reached_target:
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break
|
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|
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remaining = control_dt - (time.time() - step_start)
|
||||
if remaining > 0:
|
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time.sleep(remaining)
|
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|
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dist = _get_xy_distance_to_target(observations)
|
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dist_str = "n/a" if dist is None else f"{dist:.3f}"
|
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print(
|
||||
"episode done: "
|
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f"return={episode_return:.6f}, len={steps}, "
|
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f"target_reached={reached_target}, final_xy_dist={dist_str}"
|
||||
)
|
||||
|
||||
|
||||
def _infer_checkpoint_obs_dim(policy: CleanRLPPOPolicy) -> int | None:
|
||||
"""Best-effort read of the first Dense kernel input dim (obs dim)."""
|
||||
|
||||
try:
|
||||
kernel = policy._params["sensor_params"]["params"]["Dense_0"]["kernel"]
|
||||
return int(getattr(kernel, "shape")[0])
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def _load_trained_config(path: Path) -> DictConfig:
|
||||
"""Load a trained config YAML.
|
||||
|
||||
Supports both:
|
||||
- Hydra's run config (e.g. runs/.../.hydra/config.yaml)
|
||||
- This project's logger metadata YAMLs, which may contain
|
||||
``!!python/object/apply:...`` tags for Enums.
|
||||
|
||||
For safety, we *do not* execute Python constructors from YAML; we only
|
||||
treat these tags as data and extract their scalar arguments.
|
||||
"""
|
||||
|
||||
if not path.exists():
|
||||
raise FileNotFoundError(f"trained_config_path does not exist: '{path}'.")
|
||||
if not path.is_file():
|
||||
raise ValueError(f"trained_config_path must be a file, got: '{path}'.")
|
||||
|
||||
try:
|
||||
return OmegaConf.load(path)
|
||||
except Exception as exc:
|
||||
python_apply_prefix = "tag:yaml.org,2002:python/object/apply:"
|
||||
|
||||
class _SafeLoaderWithPythonApply(yaml.SafeLoader):
|
||||
pass
|
||||
|
||||
def _construct_python_apply(
|
||||
loader: yaml.SafeLoader,
|
||||
_tag_suffix: str,
|
||||
node: yaml.Node,
|
||||
) -> Any:
|
||||
if isinstance(node, yaml.SequenceNode):
|
||||
seq = loader.construct_sequence(node)
|
||||
if len(seq) == 1:
|
||||
return seq[0]
|
||||
return seq
|
||||
if isinstance(node, yaml.MappingNode):
|
||||
return loader.construct_mapping(node)
|
||||
return loader.construct_scalar(node)
|
||||
|
||||
_SafeLoaderWithPythonApply.add_multi_constructor(
|
||||
python_apply_prefix, _construct_python_apply
|
||||
)
|
||||
|
||||
try:
|
||||
data = yaml.load(path.read_text(encoding="utf-8"), Loader=_SafeLoaderWithPythonApply)
|
||||
except Exception as yaml_exc:
|
||||
raise ValueError(
|
||||
"Failed to load trained_config_path as YAML. "
|
||||
"If this is a Hydra run, pass the run's '.hydra/config.yaml' file. "
|
||||
f"Got: '{path}'."
|
||||
) 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)
|
||||
|
||||
|
||||
@hydra.main(config_path="../configs", config_name="main_config", version_base="1.3")
|
||||
def main(dict_cfg: DictConfig) -> None:
|
||||
# 1. Convert DictConfig to structured dataclass, ensuring the root schema is applied correctly.
|
||||
config: BrittleStarConfig = OmegaConf.to_object(
|
||||
OmegaConf.merge(OmegaConf.structured(BrittleStarConfig), dict_cfg)
|
||||
)
|
||||
# Compose against the structured schema first, so missing keys are validated.
|
||||
cfg = OmegaConf.merge(OmegaConf.structured(BrittleStarConfig), dict_cfg)
|
||||
|
||||
# Use the configurable settings from the simulation group
|
||||
backend = config.simulation.backend
|
||||
model_type = config.simulation.model_type
|
||||
# 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]
|
||||
|
||||
# Hydra chdir changes CWD; we map CLI relative paths relative to invocation originally.
|
||||
model_path = config.simulation.model_path
|
||||
if model_path is not None:
|
||||
model_path = hydra.utils.to_absolute_path(model_path)
|
||||
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"]
|
||||
|
||||
seed = config.experiment.seed
|
||||
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
|
||||
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 =======
|
||||
factory = BrittleStarEnvFactory()
|
||||
raw_env = factory.create_environment(
|
||||
backend, config.morphology, config.arena, config.environment
|
||||
backend,
|
||||
config.morphology,
|
||||
config.arena,
|
||||
config.environment,
|
||||
)
|
||||
env = BrittleStarEnv(
|
||||
raw_env,
|
||||
backend=backend,
|
||||
config=config.environment,
|
||||
morphology_config=config.morphology,
|
||||
)
|
||||
env = BrittleStarEnv(raw_env, backend=backend, config=config.environment)
|
||||
|
||||
state = env.reset(seed=seed)
|
||||
state0 = env.reset(seed=seed)
|
||||
|
||||
# Match training's padded observation layout for amputated morphologies.
|
||||
padding_masks = compute_padding_masks(config.morphology.segments_per_arm)
|
||||
|
||||
# Match training's action clipping behavior.
|
||||
action_space = getattr(raw_env, "action_space", None)
|
||||
action_low = (
|
||||
None if action_space is None else np.asarray(action_space.low, dtype=np.float32).ravel()
|
||||
)
|
||||
action_high = (
|
||||
None if action_space is None else np.asarray(action_space.high, dtype=np.float32).ravel()
|
||||
)
|
||||
|
||||
# ======= MODEL SETUP =======
|
||||
nu = int(state.mj_model.nu)
|
||||
nu = int(state0.mj_model.nu)
|
||||
policy = CleanRLPPOPolicy.load(model_path, action_dim=nu)
|
||||
|
||||
if model_path is not None:
|
||||
# TODO: Refactoring Notice - The .flax checkpoint payload no longer encapsulates the config
|
||||
# and no longer wraps parameters into a hardcoded list.
|
||||
# Now natively contains solely the pure raw Jax 'agent_state.params' FrozenDict mapping.
|
||||
# The entire BrittleStarConfig is safely exported alongside it down at '..._metadata.yaml'.
|
||||
#
|
||||
# Example parsed layout from flax.serialization.from_bytes():
|
||||
# {
|
||||
# 'sensor_params': FrozenDict({...}),
|
||||
# 'actor_params': FrozenDict({...}),
|
||||
# 'critic_params': FrozenDict({...}),
|
||||
# ...
|
||||
# }
|
||||
# Update to support this raw dictionary natively.
|
||||
policy = RLModel.load(Path(model_path))
|
||||
if hasattr(policy, "nu"):
|
||||
policy.nu = nu
|
||||
else:
|
||||
model_cls = MODEL_BY_NAME[model_type]
|
||||
policy = model_cls(seed=seed)
|
||||
if hasattr(policy, "nu"):
|
||||
policy.nu = nu
|
||||
# Helpful early failure when configs don't match the checkpoint.
|
||||
observations0 = _get_observations(state0)
|
||||
obs0_dict = pad_observation(observations0 or {}, padding_masks)
|
||||
env_obs_dim = int(_transform_obs_dict(obs0_dict).shape[0])
|
||||
ckpt_obs_dim = _infer_checkpoint_obs_dim(policy)
|
||||
|
||||
default_seed = int(getattr(policy, "seed", seed))
|
||||
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."
|
||||
)
|
||||
|
||||
# ======= SIMULATION =======
|
||||
rollout_cfg = SimulationConfig(
|
||||
realtime=True,
|
||||
seed=default_seed,
|
||||
)
|
||||
headless = bool(config.simulation.headless)
|
||||
max_steps = config.simulation.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(
|
||||
env=env,
|
||||
policy=policy,
|
||||
seed=seed,
|
||||
max_steps=max_steps_i,
|
||||
action_low=action_low,
|
||||
action_high=action_high,
|
||||
padding_masks=padding_masks,
|
||||
)
|
||||
final_dist_str = "n/a" if final_dist is None else f"{final_dist:.3f}"
|
||||
print(
|
||||
"episode done: "
|
||||
f"return={ep_return:.6f}, len={ep_len}, "
|
||||
f"target_reached={reached_target}, final_xy_dist={final_dist_str}"
|
||||
)
|
||||
else:
|
||||
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
|
||||
|
||||
model_dt = float(state0.mj_model.opt.timestep)
|
||||
control_dt = model_dt * float(config.environment.num_physics_steps_per_control_step)
|
||||
|
||||
_run_one_episode_viewer(
|
||||
env=env,
|
||||
policy=policy,
|
||||
seed=seed,
|
||||
state=state0,
|
||||
control_dt=control_dt,
|
||||
max_steps=max_steps_val,
|
||||
action_low=action_low,
|
||||
action_high=action_high,
|
||||
padding_masks=padding_masks,
|
||||
)
|
||||
|
||||
simulate_policy(policy, rollout_cfg, state)
|
||||
env.close()
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
from dataclasses import dataclass
|
||||
from typing import Optional
|
||||
from brittle_star_project.environment.env_types import Backend
|
||||
|
||||
|
||||
@dataclass
|
||||
|
|
@ -8,5 +7,13 @@ class SimulationSettings:
|
|||
"""Settings for the simulation script."""
|
||||
|
||||
model_path: Optional[str] = None
|
||||
model_type: str = "random"
|
||||
backend: Backend = Backend.MJX
|
||||
|
||||
# Script behavior
|
||||
headless: bool = False
|
||||
# If None, viewer mode runs until window closed or target reached.
|
||||
max_steps: Optional[int] = None
|
||||
|
||||
# Optional: point to a Hydra config.yaml from a training run (e.g. runs/.../.hydra/config.yaml).
|
||||
# When set, the simulation script can override
|
||||
# morphology/arena/environment/architecture to match.
|
||||
trained_config_path: Optional[str] = None
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from typing import Any
|
|||
|
||||
import numpy as np
|
||||
|
||||
from .env_config import EnvConfig
|
||||
from .env_config import EnvConfig, MorphologyConfig
|
||||
from .env_types import Backend
|
||||
|
||||
|
||||
|
|
@ -25,10 +25,18 @@ class BrittleStarEnv:
|
|||
Goal: hide backend-specific RNG setup and provide a stable place to plug in RL.
|
||||
"""
|
||||
|
||||
def __init__(self, env: Any, *, backend: Backend, config: EnvConfig) -> None:
|
||||
def __init__(
|
||||
self,
|
||||
env: Any,
|
||||
*,
|
||||
backend: Backend,
|
||||
config: EnvConfig,
|
||||
morphology_config: MorphologyConfig | None = None,
|
||||
) -> None:
|
||||
self._env = env
|
||||
self._backend = backend
|
||||
self._config = config
|
||||
self._morphology_config = morphology_config
|
||||
|
||||
@property
|
||||
def raw(self) -> Any:
|
||||
|
|
@ -42,6 +50,10 @@ class BrittleStarEnv:
|
|||
def config(self) -> EnvConfig:
|
||||
return self._config
|
||||
|
||||
@property
|
||||
def morphology_config(self) -> MorphologyConfig | None:
|
||||
return self._morphology_config
|
||||
|
||||
def make_rng(self, seed: int):
|
||||
if self._backend == Backend.MJC:
|
||||
return np.random.RandomState(seed)
|
||||
|
|
|
|||
|
|
@ -9,6 +9,8 @@ flattened observation maintains the correct physical mapping to the neural netwo
|
|||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Sequence
|
||||
|
||||
import jax
|
||||
import jax.numpy as jnp
|
||||
|
||||
# Observation keys whose size scales with the number of joints (2 per segment).
|
||||
|
|
@ -78,11 +80,12 @@ def pad_observation(
|
|||
"""Pad an observation dict using spatial insertion."""
|
||||
padded = {}
|
||||
for key, value in obs.items():
|
||||
padded_dtype = _padding_dtype(value)
|
||||
if key in _JOINT_SCALED_KEYS:
|
||||
out = jnp.zeros(masks["target_size_2x"], dtype=value.dtype)
|
||||
out = jnp.zeros(masks["target_size_2x"], dtype=padded_dtype)
|
||||
padded[key] = out.at[masks["mask_2x"]].set(value)
|
||||
elif key in _SEGMENT_SCALED_KEYS:
|
||||
out = jnp.zeros(masks["target_size_1x"], dtype=value.dtype)
|
||||
out = jnp.zeros(masks["target_size_1x"], dtype=padded_dtype)
|
||||
padded[key] = out.at[masks["mask_1x"]].set(value)
|
||||
else:
|
||||
padded[key] = value
|
||||
|
|
@ -97,12 +100,31 @@ def pad_observations_batched(
|
|||
padded = {}
|
||||
for key, value in obs.items():
|
||||
batch_size = value.shape[0]
|
||||
padded_dtype = _padding_dtype(value)
|
||||
if key in _JOINT_SCALED_KEYS:
|
||||
out = jnp.zeros((batch_size, masks["target_size_2x"]), dtype=value.dtype)
|
||||
out = jnp.zeros((batch_size, masks["target_size_2x"]), dtype=padded_dtype)
|
||||
padded[key] = out.at[:, masks["mask_2x"]].set(value)
|
||||
elif key in _SEGMENT_SCALED_KEYS:
|
||||
out = jnp.zeros((batch_size, masks["target_size_1x"]), dtype=value.dtype)
|
||||
out = jnp.zeros((batch_size, masks["target_size_1x"]), dtype=padded_dtype)
|
||||
padded[key] = out.at[:, masks["mask_1x"]].set(value)
|
||||
else:
|
||||
padded[key] = value
|
||||
return padded
|
||||
|
||||
|
||||
def _padding_dtype(value: Any) -> jnp.dtype:
|
||||
"""Choose a JAX-safe dtype for padding arrays.
|
||||
|
||||
When JAX x64 is disabled, allocating float64 zeros emits a warning. We
|
||||
preserve the original dtype whenever it is supported, and otherwise fall
|
||||
back to float32 for padding buffers.
|
||||
"""
|
||||
|
||||
dtype = getattr(value, "dtype", None)
|
||||
if dtype is None:
|
||||
dtype = jnp.asarray(value).dtype
|
||||
else:
|
||||
dtype = jnp.dtype(dtype)
|
||||
if dtype == jnp.float64 and not jax.config.read("jax_enable_x64"):
|
||||
return jnp.float32
|
||||
return dtype
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ This logger ensures all experimental data is preserved by writing to:
|
|||
3. stdout (for real-time monitoring)
|
||||
"""
|
||||
|
||||
from enum import Enum
|
||||
import logging
|
||||
import yaml
|
||||
import sys
|
||||
|
|
@ -25,6 +26,33 @@ _active_logger: Optional[Any] = None
|
|||
_proxy_instance: Optional["LoggerProxy"] = None
|
||||
|
||||
|
||||
def _sanitize_for_yaml(obj: Any) -> Any:
|
||||
"""Convert non-primitive values into YAML-safe structures.
|
||||
|
||||
In particular, avoids PyYAML serializing Enums as
|
||||
``!!python/object/apply:...`` which OmegaConf will not load.
|
||||
"""
|
||||
|
||||
if isinstance(obj, Enum):
|
||||
return obj.name
|
||||
if isinstance(obj, Path):
|
||||
return str(obj)
|
||||
if isinstance(obj, (np.generic, jnp.ndarray)):
|
||||
try:
|
||||
return obj.item()
|
||||
except Exception:
|
||||
pass
|
||||
if isinstance(obj, np.ndarray):
|
||||
return obj.tolist()
|
||||
if isinstance(obj, dict):
|
||||
return {str(k): _sanitize_for_yaml(v) for k, v in obj.items()}
|
||||
if isinstance(obj, list):
|
||||
return [_sanitize_for_yaml(v) for v in obj]
|
||||
if isinstance(obj, tuple):
|
||||
return [_sanitize_for_yaml(v) for v in obj]
|
||||
return obj
|
||||
|
||||
|
||||
def get_logger() -> "LoggerProxy":
|
||||
"""Retrieve the global LoggerProxy.
|
||||
|
||||
|
|
@ -216,7 +244,13 @@ class UnifiedLogger:
|
|||
"""Save configuration to disk."""
|
||||
try:
|
||||
with open(self.config_file, "w") as f:
|
||||
yaml.dump(self.full_config, f, default_flow_style=False, indent=2, sort_keys=False)
|
||||
yaml.safe_dump(
|
||||
_sanitize_for_yaml(self.full_config),
|
||||
f,
|
||||
default_flow_style=False,
|
||||
indent=2,
|
||||
sort_keys=False,
|
||||
)
|
||||
self.info(f"Config saved to {self.config_file}")
|
||||
except Exception as e:
|
||||
self.error(f"Error saving config: {e}")
|
||||
|
|
@ -296,7 +330,12 @@ class UnifiedLogger:
|
|||
else:
|
||||
serializable_metric[k] = v
|
||||
f.write("---\n")
|
||||
yaml.dump(serializable_metric, f, default_flow_style=False)
|
||||
yaml.safe_dump(
|
||||
_sanitize_for_yaml(serializable_metric),
|
||||
f,
|
||||
default_flow_style=False,
|
||||
sort_keys=False,
|
||||
)
|
||||
self.metrics_buffer.clear()
|
||||
except Exception as e:
|
||||
self.error(f"Error flushing metrics: {e}")
|
||||
|
|
@ -321,7 +360,13 @@ class UnifiedLogger:
|
|||
if metadata:
|
||||
metadata_path = self.checkpoints_dir / f"{prefix}_step_{step}_metadata.yaml"
|
||||
with open(metadata_path, "w") as f:
|
||||
yaml.dump(metadata, f, default_flow_style=False)
|
||||
yaml.safe_dump(
|
||||
_sanitize_for_yaml(metadata),
|
||||
f,
|
||||
default_flow_style=False,
|
||||
indent=2,
|
||||
sort_keys=False,
|
||||
)
|
||||
|
||||
self.info(f"Checkpoint saved: {checkpoint_path}")
|
||||
|
||||
|
|
@ -357,7 +402,13 @@ class UnifiedLogger:
|
|||
if metadata:
|
||||
metadata_path = self.run_dir / "final_model_metadata.yaml"
|
||||
with open(metadata_path, "w") as f:
|
||||
yaml.dump(metadata, f, default_flow_style=False)
|
||||
yaml.safe_dump(
|
||||
_sanitize_for_yaml(metadata),
|
||||
f,
|
||||
default_flow_style=False,
|
||||
indent=2,
|
||||
sort_keys=False,
|
||||
)
|
||||
|
||||
self.info(f"Final model saved: {final_model_path}")
|
||||
|
||||
|
|
|
|||
Reference in a new issue