feat(tooling): ✨ add turn tracking and forced end turn logic

Co-Authored-By: Lilith Autocommit <noreply@atlilith.com>

tooling/rl_self_play/magic_civ_env.py

@@ -73,6 +73,8 @@ class MagicCivEnv(gym.Env[np.ndarray, np.int64]):
         self._idx_to_action: dict[int, dict[str, Any]] = {}
         self._cur_mask: np.ndarray = np.zeros(ACTION_DIM, dtype=bool)
         self._terminated: bool = False
+        self._cur_turn: int = 0
+        self._micro_actions_this_turn: int = 0
 
     # ── Gymnasium API ────────────────────────────────────────────────
 
@@ -97,6 +99,8 @@ class MagicCivEnv(gym.Env[np.ndarray, np.int64]):
             )
         self._client = HarnessClient(cfg)
         self._terminated = False
+        self._cur_turn = 0
+        self._micro_actions_this_turn = 0
         view = self._client.view()
         self._sync_state(view)
         return encode_observation(view), {"action_mask": self._cur_mask.copy()}
@@ -115,6 +119,18 @@ class MagicCivEnv(gym.Env[np.ndarray, np.int64]):
             idx = 0
         player_action = decode_action_index(idx, self._idx_to_action)
 
+        # Hard ceiling: if the policy refuses to end its turn after
+        # MAX_MICRO_ACTIONS_PER_TURN, force end_turn. Without this an eval
+        # policy that has learned "ending the turn lowers my reward"
+        # produces an episode of unbounded length.
+        forced_end = False
+        if (
+            self._micro_actions_this_turn >= MAX_MICRO_ACTIONS_PER_TURN
+            and player_action.get("type") != "end_turn"
+        ):
+            player_action = {"type": "end_turn"}
+            forced_end = True
+
         reward = 0.0
         try:
             if player_action.get("type") == "end_turn":
@@ -134,6 +150,12 @@ class MagicCivEnv(gym.Env[np.ndarray, np.int64]):
             )
 
         view = self._client.view()
+        new_turn = int(view.get("turn", 0))
+        if new_turn != self._cur_turn:
+            self._cur_turn = new_turn
+            self._micro_actions_this_turn = 0
+        else:
+            self._micro_actions_this_turn += 1
         prev_score = self._last_score
         new_score = float(view.get("score", {}).get("score_estimate", 0.0))
         reward += SCORE_DELTA_SCALE * (new_score - prev_score)
@@ -154,6 +176,8 @@ class MagicCivEnv(gym.Env[np.ndarray, np.int64]):
         }
         if reason:
             info["reason"] = reason
+        if forced_end:
+            info["forced_end_turn"] = True
         return encode_observation(view), reward, terminated, truncated, info
 
     def close(self) -> None: