| """Two-ACT relay experiment for ATEC Task E. |
| |
| Both policies are stepped on every observation so their temporal histories stay |
| aligned with the actual rollout. The primary policy controls the reliable |
| early phase; after the score reaches the configured band, the secondary policy |
| may take over the late object_1 phase. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import os |
|
|
| from solution_act import AlgSolution as _ActSolution |
|
|
|
|
| class AlgSolution: |
| def __init__(self): |
| self._primary_path = os.environ.get("ATEC_DUAL_PRIMARY_POLICY", os.environ.get("ATEC_ACT_POLICY_PATH", "")) |
| self._secondary_path = os.environ.get("ATEC_DUAL_SECONDARY_POLICY", "") |
| if not self._secondary_path: |
| raise RuntimeError("ATEC_DUAL_SECONDARY_POLICY is required for solution_dual_act") |
|
|
| old_policy_path = os.environ.get("ATEC_ACT_POLICY_PATH") |
| try: |
| if self._primary_path: |
| os.environ["ATEC_ACT_POLICY_PATH"] = self._primary_path |
| self.primary = _ActSolution() |
| os.environ["ATEC_ACT_POLICY_PATH"] = self._secondary_path |
| self.secondary = _ActSolution() |
| finally: |
| if old_policy_path is None: |
| os.environ.pop("ATEC_ACT_POLICY_PATH", None) |
| else: |
| os.environ["ATEC_ACT_POLICY_PATH"] = old_policy_path |
|
|
| self._switch_score = float(os.environ.get("ATEC_DUAL_SWITCH_SCORE", "12.0")) |
| self._switch_act_steps = int(os.environ.get("ATEC_DUAL_SWITCH_ACT_STEPS", "900")) |
| self._mode = "primary" |
| self._step = 0 |
|
|
| def reset_episode(self): |
| self.primary.reset_episode() |
| self.secondary.reset_episode() |
| self._mode = "primary" |
| self._step = 0 |
|
|
| def get_action_spec(self): |
| return None |
|
|
| def predicts(self, obs, current_score): |
| self._step += 1 |
| primary_resp = self.primary.predicts(obs, current_score) |
| secondary_resp = self.secondary.predicts(obs, current_score) |
| primary_steps = int(getattr(self.primary, "_ts", 0)) |
| if ( |
| self._mode == "primary" |
| and float(current_score) >= self._switch_score |
| and primary_steps >= self._switch_act_steps |
| ): |
| self._mode = "secondary" |
| print( |
| f"[DUAL_ACT] switching to secondary score={float(current_score):.2f} " |
| f"primary_steps={primary_steps}", |
| flush=True, |
| ) |
| return secondary_resp if self._mode == "secondary" else primary_resp |
|
|
