fix: removed deterministic flag

experiments/simulate.py

@@ -41,8 +41,6 @@ class CleanRLPPOPolicy:
         network_params: Any,
         actor_params: Any,
         action_dim: int,
-        deterministic: bool = True,
-        seed: int = 0,
     ) -> None:
         from brittle_star_project.rl import Actor, Network
 
@@ -54,16 +52,12 @@ class CleanRLPPOPolicy:
             "network_params": network_params,
             "actor_params": actor_params,
         }
-        self._deterministic = deterministic
-        self._rng = jax.random.PRNGKey(int(seed))
 
     @staticmethod
     def load(
         path: Path,
         *,
         action_dim: int,
-        deterministic: bool,
-        seed: int,
     ) -> "CleanRLPPOPolicy":
         def _get_index(container: Any, idx: int) -> Any:
             if isinstance(container, (list, tuple)):
@@ -129,26 +123,16 @@ class CleanRLPPOPolicy:
             network_params=network_params,
             actor_params=actor_params,
             action_dim=action_dim,
-            deterministic=deterministic,
-            seed=seed,
         )
 
-    def reset(self, seed: int) -> None:
-        self._rng = jax.random.PRNGKey(int(seed))
-
     def act(self, *, observations: dict[str, Any]) -> np.ndarray:
         obs = _flatten_obs_dict(observations)
         hidden = self._network_apply(self._params["network_params"], obs)
-        mean, log_std = self._actor_apply(self._params["actor_params"], hidden)
+        mean, _log_std = self._actor_apply(self._params["actor_params"], hidden)
 
-        if self._deterministic:
-            action = mean
-        else:
-            self._rng, sub = jax.random.split(self._rng)
-            noise = jax.random.normal(sub, shape=mean.shape)
-            action = mean + noise * jnp.exp(log_std)
-
-        return np.asarray(action, dtype=np.float32).ravel()
+        # Always evaluate with the actor mean.
+        # (Sampling adds exploration noise, which is useful for training but not for evaluation.)
+        return np.asarray(mean, dtype=np.float32).ravel()
 
 
 def _get_observations(state: Any) -> dict[str, Any]:
@@ -174,8 +158,6 @@ def _rollout_one_episode_headless(
 
     Returns (return, length, reached_target, final_xy_dist).
     """
-
-    policy.reset(seed)
     state = env.reset(seed=seed)
 
     ep_return = 0.0
@@ -294,8 +276,6 @@ def _run_one_episode_viewer(
 
 
 def parse_args() -> argparse.Namespace:
-    from brittle_star_project.environment import Task
-
     p = argparse.ArgumentParser(
         description="Run a trained policy for exactly one episode (viewer or headless)."
     )
@@ -305,12 +285,6 @@ def parse_args() -> argparse.Namespace:
         required=True,
         help=("Path to a CleanRL/Flax '.cleanrl_model' checkpoint (saved by src/train.py)."),
     )
-    p.add_argument(
-        "--deterministic",
-        action=argparse.BooleanOptionalAction,
-        default=True,
-        help="Use mean action (deterministic) or sample actions (stochastic).",
-    )
     p.add_argument(
         "--headless",
         action="store_true",
@@ -369,14 +343,9 @@ def main() -> None:
     policy = CleanRLPPOPolicy.load(
         model_path,
         action_dim=nu,
-        deterministic=bool(args.deterministic),
-        seed=seed_for_env,
     )
 
-    # Reset policy RNG if a seed was provided.
     default_seed = seed_for_env
-    if args.seed is not None:
-        policy.reset(int(args.seed))
 
     # ======= SIMULATION =======