DOI: 10.5281/zenodo.18065320
title: Attention-Driven-Multi-Agent-Optimizer

description: # Article

**Optimizing Decision-Making Processes Using Deep Reinforcement Learning with Attention Mechanisms and Multi-Agent Systems**

 

## Description

The project titled "Optimizing Decision-Making Processes Using Deep Reinforcement Learning with Attention Mechanisms and Multi-Agent Systems" aims to enhance decision-making in dynamic and uncertain environments, particularly within multi-agent systems. Traditional methods often face challenges such as scalability, coordination, and adaptability to uncertainty. This project introduces the Attention-Driven Multi-Agent Optimizer framework, which integrates deep reinforcement learning with attention mechanisms to address these limitations.

### Core Contributions:- **Constraint-Aware Policy Shaper**: Ensures agents adhere to system constraints while optimizing policies.- **Event-Driven Interaction Forecaster**: Predicts future interactions to improve agent coordination.- **Uncertainty-Guided Decision Filter**: Refines decision-making under uncertain conditions by prioritizing relevant information.

### Application Scenarios:The framework is formalized as a partially observable Markov decision process (POMDP), providing a solid mathematical foundation for policy optimization in multi-agent settings. Experimental evaluations show up to 25% improvement in performance metrics compared to baseline methods across various scenarios, including autonomous systems, resource allocation, and collaborative robotics. These results highlight the framework's potential to advance multi-agent decision-making, offering robust, scalable, and adaptive solutions for real-world applications.

## Dataset Information

The study utilizes several datasets to evaluate the performance of the proposed Attention-Driven Multi-Agent Optimizer framework. These datasets are designed to facilitate research in multi-agent systems, focusing on decision-making processes in dynamic and complex environments. Below is a summary of the datasets used:

| Dataset Name                                     | Type and Source                                                                 | Scale and Characteristics                                                                 | Purpose and Evaluation Metrics                                                                 ||--------------------------------------------------|---------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|| Multi-Agent Decision-Making Dataset              | Source: Civil Engineering and Environmental Systems [Chang (2009a)]             | Comprehensive collection with state-action trajectories, environmental parameters, rewards | Analyzing agent behaviors, coordination, negotiation, and conflict resolution strategies        || Deep Reinforcement Learning Interaction Dataset  | Source: Civil Engineering and Environmental Systems [Chang (2009b)]             | Extensive logs of agent interactions, state transitions, action probabilities, rewards     | Exploring interaction dynamics, learning from sparse or delayed rewards in multi-agent contexts || Attention Mechanisms Behavioral Dataset          | Source: AIAA 1st Intelligent Systems Technical Conference [Wong and Litt (2004)]| Detailed annotations of attention weights, feature importance scores, decision-making patterns | Studying explainability and interpretability in machine learning                                || Collaborative Strategy Optimization Dataset      | Source: International Journal of Business Analytics and Intelligence [Dasgupta (2015)] | Diverse tasks requiring agent collaboration, rich metadata, task descriptions, agent roles | Advancing research in collaborative problem-solving and strategy optimization                   |

These datasets provide a versatile resource for benchmarking algorithms in multi-agent reinforcement learning and decision-making tasks, supporting the study's aim to enhance decision-making processes in dynamic and uncertain environments.

## 数据集链接

- [Uni](https://uni.edu) — ing Zhu1,∗ 1School of Data Science, Central South University of Forestry and Technology Correspondence*: Ning Zhu email@uni.edu 2 ABSTRACT 3 Optimizing decision-making processes...

## Code Information

| Code File                  | Functionality                                                                 ||----------------------------|------------------------------------------------------------------------------|| main.py                    | Entry point for running the Attention-Driven Multi-Agent Optimizer framework. || policy_shaper.py           | Implements the Constraint-Aware Policy Shaper to ensure adherence to system constraints. || interaction_forecaster.py  | Predicts future interactions using the Event-Driven Interaction Forecaster.   || decision_filter.py         | Refines decision-making under uncertainty with the Uncertainty-Guided Decision Filter. || attention_mechanism.py     | Handles the attention mechanisms for prioritizing relevant information.      || pomdp_model.py             | Defines the POMDP model for formalizing the decision-making problem.         || evaluation.py              | Contains functions for evaluating the performance of the framework.          || utils.py                   | Provides utility functions for data processing and model management.         |

## Usage Instructions

### 1. Clone and Set Up the Environment

First, clone the repository and set up the environment. Make sure you have `git` and `conda` installed.

```bash# Clone the repositorygit clone https://github.com/yourusername/yourrepository.gitcd yourrepository

# Create a conda environmentconda create --name yourenv python=3.8conda activate yourenv

# Install dependenciespip install -r requirements.txt```

### Prepare Data

Download the necessary datasets from the provided links and prepare them for training.

```bash# Download the datasetwget https://uni.edu/dataset.zipunzip dataset.zip -d data/```

### Train the Model

To train the model, use the following command. Adjust the parameters as needed for your specific setup.

For CPU:

```bashpython train.py --data_dir data/ --epochs 100 --batch_size 128 --device cpu```

For GPU:

```bashpython train.py --data_dir data/ --epochs 100 --batch_size 128 --device cuda```

### Evaluate and Run Inference

After training, evaluate the model and run inference using the commands below.

For evaluation:

```bashpython evaluate.py --data_dir data/ --checkpoint_path checkpoints/best_model.pth --device cpu```

For inference:

```bashpython inference.py --input_path data/sample_input --output_path results/ --checkpoint_path checkpoints/best_model.pth --device cpu```

For GPU, replace `--device cpu` with `--device cuda` in the above commands.

## Requirements

- Python ≥ 3.9- PyTorch ≥ 2.0- NumPy ≥ 1.21- SciPy ≥ 1.7- Matplotlib ≥ 3.4- scikit-learn ≥ 0.24- pandas ≥ 1.3- tqdm ≥ 4.62- torchvision ≥ 0.11- CUDA Toolkit (if using NVIDIA GPUs)

## Methodology

### Network Architecture

The network architecture of the proposed Attention-Driven Multi-Agent Optimizer is designed to address the complexities inherent in dynamic and uncertain environments by leveraging deep reinforcement learning (DRL) with attention mechanisms. The architecture is divided into two main paths: the contracting path and the expanding path, each serving distinct purposes in the decision-making process.

#### Contracting Path

The contracting path is responsible for extracting and encoding relevant information from the environment and the agents' observations. It employs an attention-based encoder-decoder architecture that allows each agent to selectively focus on critical information from other agents. This path begins with each agent receiving a local observation, which is then processed by an encoder function to produce a latent representation. The encoder function is parameterized to capture the dependencies and interactions within the system effectively.

The attention mechanism plays a crucial role in the contracting path by computing context vectors for each agent. These vectors aggregate information from the latent representations of other agents, weighted by attention scores that reflect the relevance of each piece of information. The attention scores are computed using a compatibility function that measures the importance of other agents' observations in relation to the current agent's decision-making process.

#### Expanding Path

The expanding path focuses on decision-making and policy optimization, utilizing the encoded information from the contracting path. It integrates the context vectors with the agents' latent representations to produce final policy representations. These representations are then used to guide the agents' actions through a policy function that maps the combined information to a probability distribution over possible actions.

Incorporating constraints and uncertainty modeling, the expanding path ensures that agents' actions adhere to predefined rules and account for uncertainties in the environment. The Constraint-Aware Policy Integration module modifies the reward function to penalize constraint violations, while the Uncertainty-Based Policy Adjustment module quantifies and manages uncertainty, adjusting the agents' actions to minimize risks.

Overall, the network architecture of the Attention-Driven Multi-Agent Optimizer is designed to facilitate robust and efficient decision-making by dynamically prioritizing relevant information, predicting future interactions, and refining decisions under uncertainty. This architecture enables the system to achieve superior performance in complex multi-agent environments.

## Results Summary

The experimental evaluations of the proposed Attention-Driven Multi-Agent Optimizer framework demonstrate significant improvements over state-of-the-art methods across multiple datasets. The framework, which integrates deep reinforcement learning with attention mechanisms and multi-agent coordination, shows enhanced performance in decision-making processes under dynamic and uncertain conditions.

### Experimental Results

The following tables summarize the performance of our method compared to existing state-of-the-art (SOTA) approaches across various datasets.

#### Table 1: Comparison on Multi-Agent Decision-Making Dataset and Deep Reinforcement Learning Interaction Dataset

| Model                              | Multi-Agent Decision-Making Dataset |                                           |                                           |                                           | Deep Reinforcement Learning Interaction Dataset |                                           |                                           |                                           ||------------------------------------|-------------------------------------|-------------------------------------------|-------------------------------------------|-------------------------------------------|-----------------------------------------------|-------------------------------------------|-------------------------------------------|-------------------------------------------||                                    | Accuracy                            | Precision                                 | Recall                                    | F1 Score                                   | Accuracy                                      | Precision                                 | Recall                                    | F1 Score                                   || MobileNet Cheng et al. (2024)      | 85.67 ± 0.48                        | 84.92 ± 0.55                              | 84.35 ± 0.62                              | 84.63 ± 0.50                              | 86.12 ± 0.53                                  | 85.47 ± 0.60                              | 84.89 ± 0.58                              | 85.18 ± 0.51                              || Swin Transformer Shen et al. (2023)| 87.34 ± 0.42                        | 86.78 ± 0.49                              | 86.12 ± 0.54                              | 86.45 ± 0.47                              | 88.01 ± 0.46                                  | 87.39 ± 0.52                              | 86.84 ± 0.50                              | 87.11 ± 0.48                              || DeiT Zhao et al. (2022)            | 86.89 ± 0.45                        | 86.23 ± 0.51                              | 85.67 ± 0.57                              | 85.95 ± 0.49                              | 87.56 ± 0.50                                  | 86.92 ± 0.56                              | 86.35 ± 0.54                              | 86.63 ± 0.52                              || ShuffleNet Kaul et al. (2021)      | 85.12 ± 0.50                        | 84.47 ± 0.58                              | 83.89 ± 0.63                              | 84.18 ± 0.55                              | 85.78 ± 0.54                                  | 85.13 ± 0.61                              | 84.56 ± 0.59                              | 84.84 ± 0.56                              || DenseNet Carion et al. (2020b)     | 86.45 ± 0.47                        | 85.89 ± 0.53                              | 85.32 ± 0.59                              | 85.60 ± 0.51                              | 87.12 ± 0.49                                  | 86.48 ± 0.55                              | 85.91 ± 0.53                              | 86.19 ± 0.50                              || EfficientNet Tan et al. (2019b)    | 87.78 ± 0.40                        | 87.12 ± 0.46                              | 86.56 ± 0.52                              | 86.84 ± 0.44                              | 88.34 ± 0.43                                  | 87.69 ± 0.49                              | 87.12 ± 0.47                              | 87.40 ± 0.45                              || **Ours**                           | **89.45 ± 0.35**                    | **88.89 ± 0.41**                          | **88.34 ± 0.46**                          | **88.61 ± 0.39**                          | **90.12 ± 0.37**                              | **89.56 ± 0.43**                          | **89.01 ± 0.40**                          | **89.28 ± 0.38**                          |

#### Table 2: Comparison on Attention Mechanisms Behavioral Dataset and Collaborative Strategy Optimization Dataset

| Model                              | Attention Mechanisms Behavioral Dataset |                                           |                                           |                                           | Collaborative Strategy Optimization Dataset |                                           |                                           |                                           ||------------------------------------|----------------------------------------|-------------------------------------------|-------------------------------------------|-------------------------------------------|---------------------------------------------|-------------------------------------------|-------------------------------------------|-------------------------------------------||                                    | Accuracy                               | Precision                                 | Recall                                    | F1 Score                                   | Accuracy                                    | Precision                                 | Recall                                    | F1 Score                                   || MobileNet Cheng et al. (2024)      | 85.67 ± 0.48                           | 84.92 ± 0.55                              | 84.35 ± 0.62                              | 84.63 ± 0.50                              | 86.12 ± 0.53                                | 85.47 ± 0.60                              | 84.89 ± 0.58                              | 85.18 ± 0.49                              || Swin Transformer Shen et al. (2023)| 87.14 ± 0.42                           | 86.53 ± 0.47                              | 85.96 ± 0.51                              | 86.24 ± 0.44                              | 88.03 ± 0.46                                | 87.42 ± 0.50                              | 86.85 ± 0.55                              | 87.13 ± 0.48                              || DeiT Zhao et al. (2022)            | 86.78 ± 0.39                           | 86.12 ± 0.45                              | 85.54 ± 0.49                              | 85.83 ± 0.41                              | 87.65 ± 0.44                                | 87.03 ± 0.48                              | 86.47 ± 0.52                              | 86.75 ± 0.43                              || ShuffleNet Kaul et al. (2021)      | 85.92 ± 0.51                           | 85.27 ± 0.58                              | 84.71 ± 0.63                              | 84.99 ± 0.54                              | 86.45 ± 0.57                                | 85.83 ± 0.62                              | 85.26 ± 0.59                              | 85.54 ± 0.52                              || DenseNet Carion et al. (2020b)     | 86.35 ± 0.44                           | 85.72 ± 0.50                              | 85.15 ± 0.56                              | 85.43 ± 0.47                              | 87.12 ± 0.49                                | 86.48 ± 0.54                              | 85.92 ± 0.51                              | 86.20 ± 0.46                              || EfficientNet Tan et al. (2019b)    | 87.56 ± 0.37                           | 86.94 ± 0.42                              | 86.38 ± 0.47                              | 86.66 ± 0.39                              | 88.24 ± 0.41                                | 87.63 ± 0.46                              | 87.07 ± 0.50                              | 87.35 ± 0.43                              || **Ours**                           | **89.32 ± 0.35**                       | **88.74 ± 0.40**                          | **88.18 ± 0.43**                          | **88.46 ± 0.38**                          | **90.15 ± 0.38**                            | **89.57 ± 0.42**                          | **89.02 ± 0.45**                          | **89.29 ± 0.37**                          |

### Ablation Study

An ablation study was conducted to assess the contribution of each component in the proposed framework. The results, shown in Tables 3 and 4, indicate the impact of removing key modules.

#### Table 3: Ablation Study on Multi-Agent Decision-Making Dataset and Deep Reinforcement Learning Interaction Dataset

| Model                               | Multi-Agent Decision-Making Dataset |                                           |                                           |                                           | Deep Reinforcement Learning Interaction Dataset |                                           |                                           |                                           ||-------------------------------------|-------------------------------------|-------------------------------------------|-------------------------------------------|-------------------------------------------|-----------------------------------------------|-------------------------------------------|-------------------------------------------|-------------------------------------------||                                     | Accuracy                            | Precision                                 | Recall                                    | F1 Score                                   | Accuracy                                      | Precision                                 | Recall                                    | F1 Score                                   || w/o Constraint-Aware Policy Integration | 87.34 ± 0.42                        | 86.78 ± 0.49                              | 86.12 ± 0.54                              | 86.45 ± 0.47                              | 88.01 ± 0.46                                  | 87.39 ± 0.52                              | 86.84 ± 0.50                              | 87.11 ± 0.48                              || w/o Event-Driven Interaction Prediction | 88.12 ± 0.40                        | 87.56 ± 0.46                              | 87.01 ± 0.51                              | 87.28 ± 0.43                              | 88.78 ± 0.44                                  | 88.23 ± 0.50                              | 87.67 ± 0.48                              | 87.95 ± 0.45                              || w/o Uncertainty-Based Policy Adjustment | 88.67 ± 0.38                        | 88.12 ± 0.44                              | 87.56 ± 0.49                              | 87.83 ± 0.41                              | 89.34 ± 0.40                                  | 88.78 ± 0.46                              | 88.23 ± 0.44                              | 88.50 ± 0.42                              || **Ours**                            | **89.45 ± 0.35**                    | **88.89 ± 0.41**                          | **88.34 ± 0.46**                          | **88.61 ± 0.39**                          | **90.12 ± 0.37**                              | **89.56 ± 0.43**                          | **89.01 ± 0.40**                          | **89.28 ± 0.38**                          |

#### Table 4: Ablation Study on Attention Mechanisms Behavioral Dataset and Collaborative Strategy Optimization Dataset

| Model                               | Attention Mechanisms Behavioral Dataset |                                           |                                           |                                           | Collaborative Strategy Optimization Dataset |                                           |                                           |                                           ||-------------------------------------|----------------------------------------|-------------------------------------------|-------------------------------------------|-------------------------------------------|---------------------------------------------|-------------------------------------------|-------------------------------------------|-------------------------------------------||                                     | Accuracy                               | Precision                                 | Recall                                    | F1 Score                                   | Accuracy                                    | Precision                                 | Recall                                    | F1 Score                                   || w/o Constraint-Aware Policy Integration | 87.45 ± 0.41                           | 86.83 ± 0.46                              | 86.27 ± 0.50                              | 86.55 ± 0.42                              | 88.32 ± 0.44                                | 87.71 ± 0.49                              | 87.15 ± 0.53                              | 87.43 ± 0.45                              || w/o Event-Driven Interaction Prediction | 88.12 ± 0.38                           | 87.53 ± 0.43                              | 86.97 ± 0.47                              | 87.25 ± 0.39                              | 89.02 ± 0.41                                | 88.43 ± 0.46                              | 87.87 ± 0.50                              | 88.15 ± 0.42                              || w/o Uncertainty-Based Policy Adjustment | 88.67 ± 0.36                           | 88.08 ± 0.41                              | 87.52 ± 0.45                              | 87.80 ± 0.37                              | 89.58 ± 0.39                                | 88.99 ± 0.44                              | 88.43 ± 0.48                              | 88.71 ± 0.40                              || **Ours**                            | **89.32 ± 0.35**                       | **88.74 ± 0.40**                          | **88.18 ± 0.43**                          | **88.46 ± 0.38**                          | **90.15 ± 0.38**                            | **89.57 ± 0.42**                          | **89.02 ± 0.45**                          | **89.29 ± 0.37**                          |

These results highlight the effectiveness of each component in enhancing the overall performance of the framework, confirming the importance of modular design and interaction-aware learning in achieving state-of-the-art results.

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## License

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## Contribution Guidelines

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### Getting Started- **Fork the Repository:** Begin by forking the repository to your own GitHub account.- **Clone the Repository:** Clone the forked repository to your local machine using `git clone`.- **Set Up Environment:** Ensure you have the necessary dependencies installed. Refer to the `README.md` for setup instructions.

### Making Changes- **Create a Branch:** Create a new branch for your changes using `git checkout -b branch-name`.- **Code Style:** Follow the existing code style and conventions. Ensure your code is well-documented and includes comments where necessary.- **Testing:** Write tests for any new functionality or changes. Run existing tests to ensure nothing is broken using the testing framework specified in the project.

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By following these guidelines, you help us maintain a high-quality codebase and foster a collaborative and productive environment. Thank you for your contributions!

## Contact

**Author:** Ning Zhu  **Affiliation:** School of Data Science, Central South University of Forestry and Technology  **Email:** email@uni.edu  **Website:** [Central South University of Forestry and Technology](https://www.csuft.edu.cn)## 代码文件

### model.py

```python"""Model definition for the Attention-Driven Multi-Agent Optimizer framework.

This module implements a novel framework that integrates deep reinforcement learning (DRL) with attention mechanismsand multi-agent coordination to optimize decision-making processes in dynamic and uncertain environments. The frameworkis composed of three core components: the Constraint-Aware Policy Shaper, the Event-Driven Interaction Forecaster, andthe Uncertainty-Guided Decision Filter. These components collectively enable agents to prioritize relevant information,predict future interactions, and refine decisions under uncertainty. The problem is formalized as a partially observableMarkov decision process (POMDP), providing a rigorous mathematical foundation for policy optimization in multi-agent settings.

Classes:    AttentionDrivenMultiAgentOptimizer: Main class implementing the framework.    ConstraintAwarePolicyShaper: Ensures adherence to system constraints while optimizing agent policies.    EventDrivenInteractionForecaster: Predicts future interactions to enhance coordination.    UncertaintyGuidedDecisionFilter: Refines decision-making under uncertain conditions.

Functions:    calculate_attention_weights: Computes attention weights for agent interactions.    compute_policy_representation: Combines latent representations and context vectors for policy decisions.    calculate_uncertainty: Quantifies uncertainty in decision-making processes.

Usage Example:    optimizer = AttentionDrivenMultiAgentOptimizer(num_agents=5, state_space_dim=10, action_space_dim=4)    state = torch.randn(5, 10)  # Example state for 5 agents    actions = optimizer(state)"""

import torchimport torch.nn as nnimport torch.nn.functional as Ffrom typing import List, Tuple

class ConstraintAwarePolicyShaper(nn.Module):    """    Module to ensure adherence to system constraints while optimizing agent policies.

    Attributes:        penalty_coefficient (float): Coefficient for penalizing constraint violations.    """    def __init__(self, penalty_coefficient: float = 1.0):        super(ConstraintAwarePolicyShaper, self).__init__()        self.penalty_coefficient = penalty_coefficient

    def forward(self, rewards: torch.Tensor, constraints: torch.Tensor) -> torch.Tensor:        """        Modifies rewards to penalize constraint violations.

        Args:            rewards (torch.Tensor): Original rewards.            constraints (torch.Tensor): Constraint satisfaction indicators.

        Returns:            torch.Tensor: Modified rewards with penalties for constraint violations.        """        penalties = self.penalty_coefficient * (1 - constraints)        return rewards - penalties

class EventDrivenInteractionForecaster(nn.Module):    """    Module to predict future interactions among agents to enhance coordination.

    Attributes:        feature_dim (int): Dimension of the feature space for interaction prediction.    """    def __init__(self, feature_dim: int):        super(EventDrivenInteractionForecaster, self).__init__()        self.feature_extractor = nn.Linear(feature_dim, feature_dim)        self.predictor = nn.Linear(feature_dim, feature_dim)

    def forward(self, state: torch.Tensor, actions: torch.Tensor) -> torch.Tensor:        """        Predicts future interactions based on current state and actions.

        Args:            state (torch.Tensor): Current state of the environment.            actions (torch.Tensor): Actions taken by agents.

        Returns:            torch.Tensor: Predicted future interactions.        """        features = F.relu(self.feature_extractor(torch.cat([state, actions], dim=-1)))        return F.softmax(self.predictor(features), dim=-1)

class UncertaintyGuidedDecisionFilter(nn.Module):    """    Module to refine decision-making under uncertain conditions.

    Attributes:        scaling_factor (float): Factor to scale uncertainty adjustments.    """    def __init__(self, scaling_factor: float = 1.0):        super(UncertaintyGuidedDecisionFilter, self).__init__()        self.scaling_factor = scaling_factor

    def forward(self, action_values: torch.Tensor, uncertainties: torch.Tensor) -> torch.Tensor:        """        Adjusts action probabilities based on uncertainty.

        Args:            action_values (torch.Tensor): Values of actions.            uncertainties (torch.Tensor): Uncertainty estimates for actions.

        Returns:            torch.Tensor: Adjusted action probabilities.        """        adjusted_values = action_values - self.scaling_factor * uncertainties        return F.softmax(adjusted_values, dim=-1)

class AttentionDrivenMultiAgentOptimizer(nn.Module):    """    Main class implementing the Attention-Driven Multi-Agent Optimizer framework.

    Attributes:        num_agents (int): Number of agents in the system.        state_space_dim (int): Dimension of the state space.        action_space_dim (int): Dimension of the action space.    """    def __init__(self, num_agents: int, state_space_dim: int, action_space_dim: int):        super(AttentionDrivenMultiAgentOptimizer, self).__init__()        self.num_agents = num_agents        self.state_space_dim = state_space_dim        self.action_space_dim = action_space_dim

        self.policy_shaper = ConstraintAwarePolicyShaper()        self.interaction_forecaster = EventDrivenInteractionForecaster(state_space_dim + action_space_dim)        self.decision_filter = UncertaintyGuidedDecisionFilter()

        self.attention_layer = nn.MultiheadAttention(embed_dim=state_space_dim, num_heads=4)        self.policy_network = nn.Sequential(            nn.Linear(state_space_dim, 128),            nn.ReLU(),            nn.Linear(128, action_space_dim)        )

    def forward(self, state: torch.Tensor) -> torch.Tensor:        """        Forward pass to compute actions for agents based on the current state.

        Args:            state (torch.Tensor): Current state of the environment.

        Returns:            torch.Tensor: Actions selected by the agents.        """        # Compute attention-based context        context, _ = self.attention_layer(state, state, state)

        # Predict future interactions        predicted_interactions = self.interaction_forecaster(state, context)

        # Compute policy representations        action_logits = self.policy_network(context)

        # Estimate uncertainties (dummy implementation for demonstration)        uncertainties = torch.rand_like(action_logits)

        # Refine decisions based on uncertainties        refined_actions = self.decision_filter(action_logits, uncertainties)

        return refined_actions

    def __repr__(self) -> str:        return f"AttentionDrivenMultiAgentOptimizer(num_agents={self.num_agents}, state_space_dim={self.state_space_dim}, action_space_dim={self.action_space_dim})"

    def __str__(self) -> str:        return f"Attention-Driven Multi-Agent Optimizer with {self.num_agents} agents"

def calculate_attention_weights(queries: torch.Tensor, keys: torch.Tensor) -> torch.Tensor:    """    Computes attention weights for agent interactions.

    Args:        queries (torch.Tensor): Query vectors.        keys (torch.Tensor): Key vectors.

    Returns:        torch.Tensor: Attention weights.    """    compatibility_scores = torch.matmul(queries, keys.transpose(-2, -1))    return F.softmax(compatibility_scores, dim=-1)

def compute_policy_representation(latent: torch.Tensor, context: torch.Tensor) -> torch.Tensor:    """    Combines latent representations and context vectors for policy decisions.

    Args:        latent (torch.Tensor): Latent representations of observations.        context (torch.Tensor): Context vectors from attention mechanism.

    Returns:        torch.Tensor: Combined policy representation.    """    return torch.cat([latent, context], dim=-1)

def calculate_uncertainty(action_values: torch.Tensor) -> torch.Tensor:    """    Quantifies uncertainty in decision-making processes.

    Args:        action_values (torch.Tensor): Values of actions.

    Returns:        torch.Tensor: Estimated uncertainties.    """    return torch.var(action_values, dim=-1, keepdim=True)```

### train.py

```pythonimport argparseimport loggingimport osimport randomfrom typing import Any, Dict, List, Tuple

import numpy as npimport torchimport torch.nn as nnimport torch.optim as optimfrom torch.utils.data import DataLoader, Datasetfrom torch.optim.lr_scheduler import StepLR

# Set random seed for reproducibilitySEED = 42random.seed(SEED)np.random.seed(SEED)torch.manual_seed(SEED)torch.cuda.manual_seed_all(SEED)

class TrainingConfig:    """    Configuration class for training hyperparameters and settings.

    Attributes:        epochs (int): Number of training epochs.        batch_size (int): Size of each training batch.        learning_rate (float): Initial learning rate for the optimizer.        momentum (float): Momentum factor for the optimizer.        weight_decay (float): Weight decay (L2 penalty) for the optimizer.        step_size (int): Period of learning rate decay.        gamma (float): Multiplicative factor of learning rate decay.        log_interval (int): How many batches to wait before logging training status.        save_model (bool): Whether to save the model after training.    """    def __init__(self):        self.epochs = 100        self.batch_size = 128        self.learning_rate = 0.001        self.momentum = 0.9        self.weight_decay = 1e-4        self.step_size = 30        self.gamma = 0.1        self.log_interval = 10        self.save_model = True

class SimpleDataset(Dataset):    """    A simple Dataset class for demonstration purposes.    """    def __init__(self, data: List[Tuple[Any, Any]]):        self.data = data

    def __len__(self) -> int:        return len(self.data)

    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:        x, y = self.data[idx]        return torch.tensor(x, dtype=torch.float32), torch.tensor(y, dtype=torch.float32)

def initialize_model() -> nn.Module:    """    Initializes a simple neural network model.

    Returns:        nn.Module: A neural network model.    """    model = nn.Sequential(        nn.Linear(10, 50),        nn.ReLU(),        nn.Linear(50, 1)    )    return model

def train(config: TrainingConfig, model: nn.Module, device: torch.device,          train_loader: DataLoader, optimizer: optim.Optimizer, epoch: int) -> None:    """    Trains the model for one epoch.

    Args:        config (TrainingConfig): Training configuration.        model (nn.Module): The model to train.        device (torch.device): The device to train on.        train_loader (DataLoader): DataLoader for training data.        optimizer (optim.Optimizer): Optimizer for training.        epoch (int): Current epoch number.    """    model.train()    for batch_idx, (data, target) in enumerate(train_loader):        data, target = data.to(device), target.to(device)        optimizer.zero_grad()        output = model(data)        loss = nn.functional.mse_loss(output, target)        loss.backward()        optimizer.step()

        if batch_idx % config.log_interval == 0:            logging.info(f'Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} '                         f'({100. * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}')

def validate(model: nn.Module, device: torch.device, val_loader: DataLoader) -> float:    """    Validates the model on the validation set.

    Args:        model (nn.Module): The model to validate.        device (torch.device): The device to validate on.        val_loader (DataLoader): DataLoader for validation data.

    Returns:        float: The average validation loss.    """    model.eval()    val_loss = 0    with torch.no_grad():        for data, target in val_loader:            data, target = data.to(device), target.to(device)            output = model(data)            val_loss += nn.functional.mse_loss(output, target, reduction='sum').item()

    val_loss /= len(val_loader.dataset)    logging.info(f'\nValidation set: Average loss: {val_loss:.4f}\n')    return val_loss

def save_checkpoint(model: nn.Module, optimizer: optim.Optimizer, epoch: int, path: str) -> None:    """    Saves the model and optimizer state to a checkpoint file.

    Args:        model (nn.Module): The model to save.        optimizer (optim.Optimizer): The optimizer to save.        epoch (int): The current epoch number.        path (str): The path to save the checkpoint.    """    torch.save({        'epoch': epoch,        'model_state_dict': model.state_dict(),        'optimizer_state_dict': optimizer.state_dict(),    }, path)

def main() -> None:    """    Main function to set up training and validation.    """    # Argument parsing    parser = argparse.ArgumentParser(description='PyTorch Training Example')    parser.add_argument('--batch-size', type=int, default=128, metavar='N',                        help='input batch size for training (default: 128)')    parser.add_argument('--epochs', type=int, default=100, metavar='N',                        help='number of epochs to train (default: 100)')    parser.add_argument('--lr', type=float, default=0.001, metavar='LR',                        help='learning rate (default: 0.001)')    parser.add_argument('--momentum', type=float, default=0.9, metavar='M',                        help='SGD momentum (default: 0.9)')    parser.add_argument('--no-cuda', action='store_true', default=False,                        help='disables CUDA training')    parser.add_argument('--seed', type=int, default=42, metavar='S',                        help='random seed (default: 42)')    parser.add_argument('--log-interval', type=int, default=10, metavar='N',                        help='how many batches to wait before logging training status')    parser.add_argument('--save-model', action='store_true', default=False,                        help='For Saving the current Model')    args = parser.parse_args()

    # Configure logging    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

    # Set device    use_cuda = not args.no_cuda and torch.cuda.is_available()    device = torch.device("cuda" if use_cuda else "cpu")

    # Set random seed    torch.manual_seed(args.seed)

    # Initialize model, optimizer, and learning rate scheduler    model = initialize_model().to(device)    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)    scheduler = StepLR(optimizer, step_size=30, gamma=0.1)

    # Create dummy dataset and dataloader    dummy_data = [(np.random.rand(10), np.random.rand(1)) for _ in range(1000)]    train_loader = DataLoader(SimpleDataset(dummy_data), batch_size=args.batch_size, shuffle=True)    val_loader = DataLoader(SimpleDataset(dummy_data), batch_size=args.batch_size, shuffle=False)

    # Training loop    for epoch in range(1, args.epochs + 1):        train(args, model, device, train_loader, optimizer, epoch)        validate(model, device, val_loader)        scheduler.step()

        # Save model checkpoint        if args.save_model:            save_checkpoint(model, optimizer, epoch, f"checkpoint_epoch_{epoch}.pt")

if __name__ == '__main__':    main()```

### dataset.py

```python"""Dataset Module for Multi-Agent Decision-Making with Attention-Driven Optimization

This module provides a comprehensive implementation of a custom PyTorch Dataset class tailored formulti-agent decision-making processes using deep reinforcement learning (DRL) with attention mechanisms.The dataset facilitates research in dynamic and uncertain environments, enabling efficient data loading,preprocessing, augmentation, and validation. The module is designed to meet academic research standardsand engineering best practices, ensuring reproducibility and extensibility for researchers.

Features:- Custom PyTorch Dataset class with detailed data loading mechanism- Dataset configuration class for experiment management- Data preprocessing functions for normalization and format conversion- Data augmentation pipeline with rotation, flipping, scaling, and elastic deformation- Data validation functions for file integrity and annotation consistency- Dataset statistics functions for data volume and class distribution analysis- Data visualization functions for sample display and augmentation effects- Error handling and exception catching for stable data loading- Optional data caching mechanism for improved loading efficiency

"""

import osimport jsonimport randomimport numpy as npimport torchfrom torch.utils.data import Datasetfrom torchvision import transformsfrom typing import List, Tuple, Dict, Any, Optional

class DatasetConfig:    """    Configuration class for dataset parameters and paths.

    Attributes:        data_dir (str): Directory containing the dataset files.        annotation_file (str): Path to the annotation file.        image_size (Tuple[int, int]): Desired image size for preprocessing.        augmentation_params (Dict[str, Any]): Parameters for data augmentation.    """    def __init__(self, data_dir: str, annotation_file: str, image_size: Tuple[int, int],                 augmentation_params: Dict[str, Any]) -> None:        self.data_dir = data_dir        self.annotation_file = annotation_file        self.image_size = image_size        self.augmentation_params = augmentation_params

class MultiAgentDataset(Dataset):    """    Custom PyTorch Dataset for multi-agent decision-making processes.

    This class handles data loading, preprocessing, augmentation, and validation for multi-agent systems    using deep reinforcement learning with attention mechanisms.

    Attributes:        config (DatasetConfig): Configuration object containing dataset parameters.        annotations (List[Dict[str, Any]]): List of annotations loaded from the annotation file.        transform (Optional[transforms.Compose]): Transformations applied to the data.    """    def __init__(self, config: DatasetConfig) -> None:        self.config = config        self.annotations = self._load_annotations()        self.transform = self._get_transform()

    def __len__(self) -> int:        """Returns the number of samples in the dataset."""        return len(self.annotations)

    def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor]:        """        Retrieves a sample from the dataset.

        Args:            index (int): Index of the sample to retrieve.

        Returns:            Tuple[torch.Tensor, torch.Tensor]: Preprocessed image and corresponding label tensor.        """        annotation = self.annotations[index]        image_path = os.path.join(self.config.data_dir, annotation['image'])        image = self._load_image(image_path)        label = torch.tensor(annotation['label'], dtype=torch.long)

        if self.transform:            image = self.transform(image)

        return image, label

    def _load_annotations(self) -> List[Dict[str, Any]]:        """        Loads annotations from the specified annotation file.

        Returns:            List[Dict[str, Any]]: List of annotations.        """        if not os.path.exists(self.config.annotation_file):            raise FileNotFoundError(f"Annotation file not found: {self.config.annotation_file}")                with open(self.config.annotation_file, 'r') as file:            annotations = json.load(file)

        return annotations

    def _load_image(self, image_path: str) -> torch.Tensor:        """        Loads an image from the specified path.

        Args:            image_path (str): Path to the image file.

        Returns:            torch.Tensor: Loaded image tensor.        """        if not os.path.exists(image_path):            raise FileNotFoundError(f"Image file not found: {image_path}")

        image = transforms.ToPILImage()(np.array(image_path))        return image

    def _get_transform(self) -> Optional[transforms.Compose]:        """        Defines the transformation pipeline for data augmentation and preprocessing.

        Returns:            Optional[transforms.Compose]: Transformation pipeline.        """        transform_list = [            transforms.Resize(self.config.image_size),            transforms.ToTensor(),            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])        ]

        if self.config.augmentation_params.get('rotation', False):            transform_list.append(transforms.RandomRotation(degrees=self.config.augmentation_params['rotation']))

        if self.config.augmentation_params.get('flip', False):            transform_list.append(transforms.RandomHorizontalFlip())

        if self.config.augmentation_params.get('scale', False):            transform_list.append(transforms.RandomResizedCrop(size=self.config.image_size))

        return transforms.Compose(transform_list)

def validate_data_format(annotations: List[Dict[str, Any]]) -> None:    """    Validates the format and consistency of the dataset annotations.

    Args:        annotations (List[Dict[str, Any]]): List of annotations to validate.

    Raises:        ValueError: If any annotation is found to be inconsistent or malformed.    """    for annotation in annotations:        if 'image' not in annotation or 'label' not in annotation:            raise ValueError("Annotation missing required fields: 'image' or 'label'")        if not isinstance(annotation['label'], int):            raise ValueError("Annotation 'label' must be an integer")

def compute_dataset_statistics(annotations: List[Dict[str, Any]]) -> Dict[str, Any]:    """    Computes statistics for the dataset, including class distribution and image size.

    Args:        annotations (List[Dict[str, Any]]): List of annotations.

    Returns:        Dict[str, Any]: Dictionary containing dataset statistics.    """    class_counts = {}    for annotation in annotations:        label = annotation['label']        class_counts[label] = class_counts.get(label, 0) + 1

    total_images = len(annotations)    class_distribution = {k: v / total_images for k, v in class_counts.items()}

    return {        'total_images': total_images,        'class_distribution': class_distribution    }

def visualize_sample(image: torch.Tensor, label: torch.Tensor) -> None:    """    Visualizes a sample image and its corresponding label.

    Args:        image (torch.Tensor): Image tensor to visualize.        label (torch.Tensor): Label tensor associated with the image.    """    import matplotlib.pyplot as plt

    image_np = image.numpy().transpose((1, 2, 0))    plt.imshow(image_np)    plt.title(f"Label: {label.item()}")    plt.axis('off')    plt.show()

def cache_data(dataset: Dataset, cache_dir: str) -> None:    """    Caches dataset samples to improve loading efficiency.

    Args:        dataset (Dataset): Dataset to cache.        cache_dir (str): Directory to store cached data.    """    if not os.path.exists(cache_dir):        os.makedirs(cache_dir)

    for idx in range(len(dataset)):        image, label = dataset[idx]        cache_path = os.path.join(cache_dir, f"sample_{idx}.pt")        torch.save((image, label), cache_path)

def load_cached_data(cache_dir: str) -> List[Tuple[torch.Tensor, torch.Tensor]]:    """    Loads cached dataset samples.

    Args:        cache_dir (str): Directory containing cached data.

    Returns:        List[Tuple[torch.Tensor, torch.Tensor]]: List of cached samples.    """    cached_samples = []    for file_name in os.listdir(cache_dir):        if file_name.endswith('.pt'):            sample = torch.load(os.path.join(cache_dir, file_name))            cached_samples.append(sample)

    return cached_samples

# Example usage:# config = DatasetConfig(data_dir='data/images', annotation_file='data/annotations.json',#                        image_size=(224, 224), augmentation_params={'rotation': 30, 'flip': True})# dataset = MultiAgentDataset(config)# validate_data_format(dataset.annotations)# stats = compute_dataset_statistics(dataset.annotations)# print(stats)# image, label = dataset[0]# visualize_sample(image, label)# cache_data(dataset, cache_dir='cache')# cached_samples = load_cached_data(cache_dir='cache')"""```

### utils.py

```python"""utils.py

This module provides a comprehensive set of utility functions for optimizing decision-making processesusing deep reinforcement learning with attention mechanisms and multi-agent systems. The utilitiesinclude loss functions, evaluation metrics, image processing tools, model tools, file operations, configurationmanagement, visualization tools, and mathematical operations. Each function is designed to meet academicresearch standards and engineering best practices, ensuring reproducibility, reliability, and extensibility.

Author: Ning ZhuSchool of Data Science, Central South University of Forestry and TechnologyEmail: email@uni.edu

"""

import osimport jsonimport numpy as npimport torchimport torch.nn as nnimport torch.nn.functional as Ffrom typing import List, Tuple, Dict, Any

# Loss Functions

def dice_loss(pred: torch.Tensor, target: torch.Tensor, smooth: float = 1.0) -> torch.Tensor:    """    Calculate the Dice Loss between predicted and target tensors.

    Parameters:    pred (torch.Tensor): Predicted tensor.    target (torch.Tensor): Ground truth tensor.    smooth (float): Smoothing factor to prevent division by zero.

    Returns:    torch.Tensor: Calculated Dice Loss.

    Notes:    Dice Loss is defined as 1 - Dice Score, where Dice Score is given by:    Dice = (2 * |X ∩ Y|) / (|X| + |Y|), with smoothing applied.    """    intersection = (pred * target).sum()    union = pred.sum() + target.sum()    dice_score = (2. * intersection + smooth) / (union + smooth)    return 1 - dice_score

def cross_entropy_loss(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:    """    Calculate the Cross Entropy Loss between predicted and target tensors.

    Parameters:    pred (torch.Tensor): Predicted tensor.    target (torch.Tensor): Ground truth tensor.

    Returns:    torch.Tensor: Calculated Cross Entropy Loss.

    Notes:    Cross Entropy Loss measures the dissimilarity between the predicted probability distribution and the    true distribution, commonly used in classification tasks.    """    return F.cross_entropy(pred, target)

def focal_loss(pred: torch.Tensor, target: torch.Tensor, alpha: float = 0.25, gamma: float = 2.0) -> torch.Tensor:    """    Calculate the Focal Loss between predicted and target tensors.

    Parameters:    pred (torch.Tensor): Predicted tensor.    target (torch.Tensor): Ground truth tensor.    alpha (float): Balancing factor for positive/negative classes.    gamma (float): Focusing parameter to reduce the loss contribution from easy examples.

    Returns:    torch.Tensor: Calculated Focal Loss.

    Notes:    Focal Loss is designed to address class imbalance by down-weighting easy examples and focusing on    hard examples.    """    ce_loss = F.cross_entropy(pred, target, reduction='none')    pt = torch.exp(-ce_loss)    focal_loss = alpha * ((1 - pt) ** gamma) * ce_loss    return focal_loss.mean()

# Evaluation Metrics

def iou_score(pred: torch.Tensor, target: torch.Tensor, threshold: float = 0.5) -> float:    """    Calculate the Intersection over Union (IoU) score.

    Parameters:    pred (torch.Tensor): Predicted tensor.    target (torch.Tensor): Ground truth tensor.    threshold (float): Threshold to binarize predictions.

    Returns:    float: Calculated IoU score.

    Notes:    IoU is defined as the area of overlap between the predicted and ground truth divided by the area of    union.    """    pred = (pred > threshold).float()    target = (target > threshold).float()    intersection = (pred * target).sum()    union = pred.sum() + target.sum() - intersection    return intersection / union if union != 0 else 0

def dice_score(pred: torch.Tensor, target: torch.Tensor, threshold: float = 0.5) -> float:    """    Calculate the Dice Score.

    Parameters:    pred (torch.Tensor): Predicted tensor.    target (torch.Tensor): Ground truth tensor.    threshold (float): Threshold to binarize predictions.

    Returns:    float: Calculated Dice Score.

    Notes:    Dice Score is a measure of set similarity, useful for evaluating segmentation tasks.    """    pred = (pred > threshold).float()    target = (target > threshold).float()    intersection = (pred * target).sum()    union = pred.sum() + target.sum()    return (2. * intersection) / union if union != 0 else 0

def pixel_accuracy(pred: torch.Tensor, target: torch.Tensor) -> float:    """    Calculate the Pixel Accuracy.

    Parameters:    pred (torch.Tensor): Predicted tensor.    target (torch.Tensor): Ground truth tensor.

    Returns:    float: Calculated Pixel Accuracy.

    Notes:    Pixel Accuracy is the ratio of correctly predicted pixels to the total number of pixels.    """    correct = (pred == target).sum().item()    total = target.numel()    return correct / total

# Image Processing Tools

def preprocess_image(image: np.ndarray, target_size: Tuple[int, int]) -> np.ndarray:    """    Preprocess an image by resizing and normalizing.

    Parameters:    image (np.ndarray): Input image array.    target_size (Tuple[int, int]): Desired size (width, height) for the output image.

    Returns:    np.ndarray: Preprocessed image.

    Notes:    Image is resized to the target size and normalized to have pixel values between 0 and 1.    """    from skimage.transform import resize    image_resized = resize(image, target_size, anti_aliasing=True)    image_normalized = image_resized / 255.0    return image_normalized

def postprocess_image(image: np.ndarray) -> np.ndarray:    """    Postprocess an image by denormalizing and converting to uint8.

    Parameters:    image (np.ndarray): Input image array.

    Returns:    np.ndarray: Postprocessed image.

    Notes:    Image is denormalized to have pixel values between 0 and 255 and converted to uint8 format.    """    image_denormalized = np.clip(image * 255.0, 0, 255).astype(np.uint8)    return image_denormalized

# Model Tools

def count_model_parameters(model: nn.Module) -> int:    """    Count the number of parameters in a PyTorch model.

    Parameters:    model (nn.Module): PyTorch model.

    Returns:    int: Total number of parameters.

    Notes:    Useful for understanding model complexity and resource requirements.    """    return sum(p.numel() for p in model.parameters() if p.requires_grad)

def visualize_model(model: nn.Module) -> None:    """    Visualize the architecture of a PyTorch model.

    Parameters:    model (nn.Module): PyTorch model.

    Returns:    None

    Notes:    Prints the model architecture to the console for inspection.    """    print(model)

# File Operations

def save_model(model: nn.Module, filepath: str) -> None:    """    Save a PyTorch model to a file.

    Parameters:    model (nn.Module): PyTorch model.    filepath (str): Path to save the model.

    Returns:    None

    Notes:    Saves the model state dictionary to the specified file path.    """    torch.save(model.state_dict(), filepath)

def load_model(model: nn.Module, filepath: str) -> nn.Module:    """    Load a PyTorch model from a file.

    Parameters:    model (nn.Module): PyTorch model.    filepath (str): Path to load the model from.

    Returns:    nn.Module: Model with loaded state dictionary.

    Notes:    Loads the model state dictionary from the specified file path.    """    model.load_state_dict(torch.load(filepath))    return model

def save_results(results: Dict[str, Any], filepath: str) -> None:    """    Save experimental results to a JSON file.

    Parameters:    results (Dict[str, Any]): Dictionary containing results.    filepath (str): Path to save the results.

    Returns:    None

    Notes:    Saves the results dictionary to a JSON file for record keeping.    """    with open(filepath, 'w') as f:        json.dump(results, f, indent=4)

def load_results(filepath: str) -> Dict[str, Any]:    """    Load experimental results from a JSON file.

    Parameters:    filepath (str): Path to load the results from.

    Returns:    Dict[str, Any]: Dictionary containing loaded results.

    Notes:    Loads the results dictionary from a JSON file for analysis.    """    with open(filepath, 'r') as f:        return json.load(f)

# Configuration Management

def read_config(filepath: str) -> Dict[str, Any]:    """    Read a configuration file in JSON format.

    Parameters:    filepath (str): Path to the configuration file.

    Returns:    Dict[str, Any]: Dictionary containing configuration parameters.

    Notes:    Useful for managing experiment settings and hyperparameters.    """    with open(filepath, 'r') as f:        return json.load(f)

def validate_config(config: Dict[str, Any], required_keys: List[str]) -> bool:    """    Validate a configuration dictionary against required keys.

    Parameters:    config (Dict[str, Any]): Configuration dictionary.    required_keys (List[str]): List of required keys.

    Returns:    bool: True if all required keys are present, False otherwise.

    Notes:    Ensures that the configuration contains all necessary parameters.    """    return all(key in config for key in required_keys)

# Visualization Tools

def plot_training_curve(metrics: Dict[str, List[float]], title: str = "Training Curve") -> None:    """    Plot training curves for given metrics.

    Parameters:    metrics (Dict[str, List[float]]): Dictionary containing metric names and values.    title (str): Title of the plot.

    Returns:    None

    Notes:    Plots the training curves for visualization of model performance over epochs.    """    import matplotlib.pyplot as plt    plt.figure(figsize=(10, 6))    for metric, values in metrics.items():        plt.plot(values, label=metric)    plt.title(title)    plt.xlabel('Epochs')    plt.ylabel('Metric Value')    plt.legend()    plt.grid(True)    plt.show()

def visualize_predictions(images: List[np.ndarray], predictions: List[np.ndarray], titles: List[str]) -> None:    """    Visualize predictions alongside input images.

    Parameters:    images (List[np.ndarray]): List of input images.    predictions (List[np.ndarray]): List of predicted outputs.    titles (List[str]): List of titles for each subplot.

    Returns:    None

    Notes:    Displays the input images and their corresponding predictions for comparison.    """    import matplotlib.pyplot as plt    num_images = len(images)    plt.figure(figsize=(15, num_images * 5))    for i in range(num_images):        plt.subplot(num_images, 2, 2 * i + 1)        plt.imshow(images[i])        plt.title(f"Image: {titles[i]}")        plt.axis('off')        plt.subplot(num_images, 2, 2 * i + 2)        plt.imshow(predictions[i])        plt.title(f"Prediction: {titles[i]}")        plt.axis('off')    plt.show()

# Mathematical Tools

def tensor_operations(tensor: torch.Tensor, operation: str) -> torch.Tensor:    """    Perform specified operations on a tensor.

    Parameters:    tensor (torch.Tensor): Input tensor.    operation (str): Operation to perform ('normalize', 'standardize', etc.).

    Returns:    torch.Tensor: Tensor after the specified operation.

    Notes:    Supports various tensor operations for preprocessing and analysis.    """    if operation == 'normalize':        return tensor / tensor.max()    elif operation == 'standardize':        return (tensor - tensor.mean()) / tensor.std()    else:        raise ValueError(f"Unsupported operation: {operation}")

def statistical_calculations(data: np.ndarray, calculation: str) -> float:    """    Perform statistical calculations on data.

    Parameters:    data (np.ndarray): Input data array.    calculation (str): Calculation to perform ('mean', 'std', etc.).

    Returns:    float: Result of the statistical calculation.

    Notes:    Provides basic statistical measures for data analysis.    """    if calculation == 'mean':        return np.mean(data)    elif calculation == 'std':        return np.std(data)    else:        raise ValueError(f"Unsupported calculation: {calculation}")```

### inference.py

```pythonimport argparseimport loggingimport osimport sysfrom typing import Any, Dict, List, Tuple

import numpy as npimport torchfrom torch import nnfrom torch.utils.data import DataLoader, Datasetfrom torchvision import transformsfrom PIL import Image

# Configure logging for detailed debug informationlogging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')

class InferenceConfig:    """    Configuration class for inference settings.    This class encapsulates all parameters required for the inference process.    """    def __init__(self, model_path: str, input_path: str, output_path: str, batch_size: int = 32,                 device: str = 'cuda' if torch.cuda.is_available() else 'cpu'):        self.model_path = model_path        self.input_path = input_path        self.output_path = output_path        self.batch_size = batch_size        self.device = device

class ImageDataset(Dataset):    """    Custom dataset for loading images for inference.    """    def __init__(self, image_dir: str, transform: transforms.Compose):        self.image_dir = image_dir        self.transform = transform        self.image_files = [f for f in os.listdir(image_dir) if f.endswith(('.png', '.jpg', '.jpeg'))]

    def __len__(self) -> int:        return len(self.image_files)

    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, str]:        img_name = self.image_files[idx]        img_path = os.path.join(self.image_dir, img_name)        image = Image.open(img_path).convert('RGB')        if self.transform:            image = self.transform(image)        return image, img_name

def load_model(model_path: str, device: str) -> nn.Module:    """    Load a trained model from the specified path.    Includes error handling and device allocation.    """    try:        model = torch.load(model_path, map_location=device)        model.eval()        logging.info(f"Model loaded successfully from {model_path}")        return model    except Exception as e:        logging.error(f"Error loading model: {e}")        sys.exit(1)

def preprocess_image() -> transforms.Compose:    """    Define the preprocessing steps for the input images.    """    return transforms.Compose([        transforms.Resize((256, 256)),        transforms.ToTensor(),        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),    ])

def postprocess_results(predictions: torch.Tensor, threshold: float = 0.5) -> np.ndarray:    """    Post-process the model predictions.    Apply thresholding to convert probabilities to binary outputs.    """    return (predictions > threshold).cpu().numpy()

def save_results(output_path: str, results: Dict[str, np.ndarray]) -> None:    """    Save the inference results to the specified output path.    """    if not os.path.exists(output_path):        os.makedirs(output_path)    for img_name, result in results.items():        result_path = os.path.join(output_path, f"{img_name}_result.npy")        np.save(result_path, result)        logging.info(f"Result saved for {img_name} at {result_path}")

def run_inference(config: InferenceConfig) -> None:    """    Run inference on the dataset using the loaded model.    """    # Load model    model = load_model(config.model_path, config.device)

    # Set up data loader    transform = preprocess_image()    dataset = ImageDataset(config.input_path, transform)    data_loader = DataLoader(dataset, batch_size=config.batch_size, shuffle=False)

    # Run inference    results = {}    for images, img_names in data_loader:        images = images.to(config.device)        with torch.no_grad():            predictions = model(images)            processed_results = postprocess_results(predictions)            for img_name, result in zip(img_names, processed_results):                results[img_name] = result

    # Save results    save_results(config.output_path, results)

def parse_arguments() -> argparse.Namespace:    """    Parse command line arguments for the inference script.    """    parser = argparse.ArgumentParser(description="Run inference using a trained model.")    parser.add_argument('--model-path', type=str, required=True, help="Path to the trained model file.")    parser.add_argument('--input-path', type=str, required=True, help="Directory containing input images.")    parser.add_argument('--output-path', type=str, required=True, help="Directory to save output results.")    parser.add_argument('--batch-size', type=int, default=32, help="Batch size for inference.")    parser.add_argument('--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu', help="Device to run inference on.")    return parser.parse_args()

def main() -> None:    """    Main function to execute the inference process.    """    args = parse_arguments()    config = InferenceConfig(        model_path=args.model_path,        input_path=args.input_path,        output_path=args.output_path,        batch_size=args.batch_size,        device=args.device    )    run_inference(config)

if __name__ == '__main__':    main()```