♻️ full agent refactor

scripts/eval.py

@@ -75,14 +75,13 @@ def _infer_hidden_sizes(state_dict: dict[str, torch.Tensor]) -> tuple[int, ...]:
 
 
 def _infer_encoder_out_dim(state_dict: dict[str, torch.Tensor]) -> int | None:
-    """Return the history/adaptation encoder output dim, if present.
+    """Return the history encoder output dim, if present.
 
     Lets eval reconstruct an embedding policy without knowing the training
-    embedding_dim/latent_dim — read it straight from the saved weights.
+    embedding_dim — read it straight from the saved weights.
     """
-    for key in ("history_encoder.fc.weight", "adaptation_module.fc.weight"):
-        if key in state_dict:
-            return state_dict[key].shape[0]
+    if "history_encoder.fc.weight" in state_dict:
+        return state_dict["history_encoder.fc.weight"].shape[0]
     return None
 
 
@@ -92,14 +91,13 @@ def load_policy(
     action_space: spaces.Space,
     device: torch.device = torch.device("cpu"),
     history_length: int = 0,
-    rma_mode: str = "none",
     raw_obs_dim: int = 0,
 ) -> tuple[SharedMLP, RunningStandardScaler]:
     """Load a trained SharedMLP + observation normalizer from a checkpoint.
 
-    For DR + history-embedding policies (history_length > 0) or RMA deploy
-    policies (rma_mode="deploy"), the history/adaptation encoder must be
-    reconstructed too — its output dim is read back from the saved weights.
+    For DR + history-embedding policies (history_length > 0), the history
+    encoder is reconstructed too — its output dim is read back from the
+    saved weights.
 
     Returns:
         (model, state_preprocessor) ready for inference.
@@ -117,11 +115,9 @@ def load_policy(
         action_space=action_space,
         device=device,
         hidden_sizes=hidden_sizes,
-        history_length=history_length,
-        rma_mode=rma_mode,
+        history_length=history_length if enc_out else 0,
         raw_obs_dim=raw_obs_dim,
-        embedding_dim=enc_out or 32,   # legacy "none" + history
-        latent_dim=enc_out or 8,       # RMA deploy adaptation module
+        embedding_dim=enc_out or 32,
     )
     model.load_state_dict(ckpt["policy"])
     model.eval()
@@ -189,7 +185,7 @@ def _add_action_arrow(viewer, model, data, action_val: float) -> None:
 @hydra.main(version_base=None, config_path="../configs", config_name="config")
 def main(cfg: DictConfig) -> None:
     choices = HydraConfig.get().runtime.choices
-    env_name = choices.get("env", "cartpole")
+    env_name = choices.get("env", "rotary_cartpole")
     runner_name = choices.get("runner", "mujoco_single")
 
     checkpoint_path = cfg.get("checkpoint", None)
@@ -222,7 +218,6 @@ def _eval_sim(cfg: DictConfig, env_name: str, checkpoint_path: str) -> None:
     model, preprocessor = load_policy(
         checkpoint_path, runner.observation_space, runner.action_space, device,
         history_length=runner.config.history_length,
-        rma_mode=runner.config.rma_mode,
         raw_obs_dim=runner.env.observation_space.shape[0],
     )
 
@@ -311,7 +306,6 @@ def _eval_serial(cfg: DictConfig, env_name: str, checkpoint_path: str) -> None:
     model, preprocessor = load_policy(
         checkpoint_path, serial_runner.observation_space, serial_runner.action_space, device,
         history_length=serial_runner.config.history_length,
-        rma_mode=serial_runner.config.rma_mode,
         raw_obs_dim=serial_runner.env.observation_space.shape[0],
     )
 
@@ -339,9 +333,7 @@ def _eval_serial(cfg: DictConfig, env_name: str, checkpoint_path: str) -> None:
             if _reset_flag[0]:
                 _reset_flag[0] = False
                 serial_runner._send("M0")
-                serial_runner._drive_to_center()
-                serial_runner._wait_for_pendulum_still()
-                obs, _ = serial_runner.reset()
+                obs, _ = serial_runner.reset()  # drives to center + settles
                 step = 0
                 episode += 1
                 episode_reward = 0.0
@@ -376,8 +368,8 @@ def _eval_serial(cfg: DictConfig, env_name: str, checkpoint_path: str) -> None:
                     "step", n=step, reward=round(reward.item(), 3),
                     action=round(action[0, 0].item(), 2),
                     ep_reward=round(episode_reward, 1),
-                    motor_enc=state["encoder_count"],
-                    pend_deg=round(state["pendulum_angle"], 1),
+                    motor_deg=round(math.degrees(state["motor_rad"]), 1),
+                    pend_deg=round(math.degrees(state["pend_rad"]), 1),
                 )
 
             # Check for safety / disconnection.