feat(evaluate): evaluate_policy base

src/brittle_star_project/evaluation/evaluate.py

@@ -0,0 +1,58 @@
+"""MJC-based (CPU) checkpoint evaluation.
+
+This module provides the CPU-bound evaluation path using the standard MJC backend.
+It is primarily used by the `evaluate_checkpoints` CLI to compute metrics and
+render videos.
+"""
+
+from pathlib import Path
+
+import numpy as np
+
+from brittle_star_project.environment.BrittleStarJaxEnvWrapper import BrittleStarJaxEnvWrapper
+from brittle_star_project.environment.obs_processing import create_obs_processor
+from brittle_star_project.evaluation.policy import PolicyAgent
+from brittle_star_project.evaluation.rollout import EpisodeResult, rollout_headless
+
+
+def evaluate_policy(
+    env: BrittleStarJaxEnvWrapper,
+    policy_path: str | Path,
+    seed: int,
+    max_steps: int,
+) -> EpisodeResult:
+    """Evaluate a trained policy in a CPU-bound environment.
+
+    Args:
+        env: Initialised CPU environment (MJC backend).
+        policy_path: Path to the ``.cleanrl_model`` weights file.
+        seed: Random seed for environment reset.
+        max_steps: Maximum number of control steps.
+
+    Returns:
+        Structured result containing return, length, and distance metrics.
+    """
+    obs_processor = create_obs_processor(
+        bounds_dict=env.cfg.obs_bounds.to_bounds_dict(),
+        padding_masks=env.padding_masks,
+    )
+
+    action_dim = env.single_action_space.shape[0]
+
+    policy = PolicyAgent.from_checkpoint(
+        model_path=Path(policy_path),
+        action_dim=action_dim,
+        obs_processor=obs_processor,
+    )
+
+    action_low = np.asarray(env.single_action_space.low, dtype=np.float32)
+    action_high = np.asarray(env.single_action_space.high, dtype=np.float32)
+
+    return rollout_headless(
+        env=env,
+        policy=policy,
+        seed=seed,
+        max_steps=max_steps,
+        action_low=action_low,
+        action_high=action_high,
+    )