minor

        score_configs = []

        for env_id, result in env_processing_results.items():

            if result["valid"] and result["gen_image"] is not None:

            # Only process valid SVGs - skip invalid ones entirely for scoring

            if result["valid"] and result["gen_image"] is not None and result["metrics"]["turn_metrics"]["svg_is_valid"]:

                valid_env_ids.append(env_id)

                gt_images.append(result["env"].gt_image)

                gen_images.append(result["gen_image"])

                env = result["env"]

                scores = batch_results[i]

                # Update reward

                env.reward += scores["total_score"]

                env.total_reward += env.reward

                # Determine if action is effective - either:

                # 1. First generation (no previous score) with a positive score

                # 2. Improved score compared to previous generation

                previous_score = 0.0

                is_first_generation = True

                if env_id in self.cache and self.cache[env_id].get('scores') is not None:

                    previous_score = self.cache[env_id]['scores'].get('total_score', 0.0)

                    is_first_generation = False

                # Check effectiveness based on whether it's the first generation or an improvement

                if is_first_generation:

                    result["metrics"]["turn_metrics"]["action_is_effective"] = scores["total_score"] > 0

                else:

                    result["metrics"]["turn_metrics"]["action_is_effective"] = scores["total_score"] > previous_score

                # Update other metrics

                result["metrics"]["turn_metrics"]["dino_score"] = scores["dino_score"]

                result["metrics"]["turn_metrics"]["dreamsim_score"] = scores["dreamsim_score"]

                info = result["rst"].copy()

                info["scores"] = scores

                info["metrics"] = result["metrics"]

                # Update cache if needed

                if env_id in self.cache:

                    self.cache[env_id]['gen_image'] = env.gen_image

                    self.cache[env_id]['gen_svg_code'] = env.gen_svg_code

                observation = env._render(init_obs=False)

                results[env_id] = serialize_step_result((observation, env.reward, False, info))

        # Handle invalid cases or cases not processed above

        for env_id, result in env_processing_results.items():

            if env_id not in results:

                env = result["env"]

                elif "traj_metrics" not in info["metrics"]:

                    info["metrics"]["traj_metrics"] = {}

                # For invalid SVGs, explicitly set scores to zero

                info["metrics"]["turn_metrics"]["action_is_valid"] = False

                info["metrics"]["turn_metrics"]["action_is_effective"] = False

                if "scores" not in info:

                # Set all scores to zero for invalid SVGs

                info["scores"] = {

                    "dino_score": 0.0,

                    "structural_score": 0.0,

                    "dreamsim_score": 0.0,

                    "total_score": 0.0

                }