Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.


 Problem Statement:

In this project, we aim to develop a reinforcement learning (RL) based solution for selecting the optimal defense strategy for image classification tasks under adversarial attacks. The goal is to integrate the Adversarial Robustness Toolbox (ART) module for both adversarial attack generation and defense strategies, and utilize the Gymnasium module to create a custom RL environment. The environment will follow typical RL steps (Reset, Step, Render, etc.) and will be trained using the PyTorch framework.

Objectives:
Attacks and Defenses:

We have a list of adversarial attacks and defense mechanisms available through the Adversarial Robustness Toolbox (ART) module.
The goal is to select the best defense strategy for each adversarial attack in the context of an image classification task.
Reinforcement Learning Approach:

RL Agent: The agent should interact with the environment and learn to choose the best defense strategy based on the attack it encounters.
Environment Design: We will design a custom environment using the Gymnasium module, where the RL agent can explore different defense strategies and optimize its performance.
Reset: Reset the environment, initializing the attack-defense setup for the episode.
Step: The agent takes an action (selecting a defense strategy) and receives feedback (reward).
Render: Visualize the learning process or the defense results.
Action: The action space consists of defense strategies available in ART.
Reward: The reward is determined based on the success of the defense against a given attack.
Observation: The agent receives observations from the environment, such as the type of attack and the classification outcome.
Done: The episode ends when the agent has tried all defenses or reached a maximum number of steps.
Terminate: Define termination criteria for the environment.
Initialize: Set up initial conditions for the attack-defense scenario.
Policy Update: Use policy gradient or Q-learning to update the agent’s policy.
Explore/Exploit: Balance exploration (trying different defenses) and exploitation (using the best-known defenses).
Evaluate/Train: Evaluate and train the agent over multiple episodes to improve its performance.
Deep Learning Framework (PyTorch):

Use PyTorch to define the neural network models for the RL agent.
Implement the policy network and value function estimation (for actor-critic methods) using PyTorch.
Train the RL agent to select the best defenses based on performance metrics.
Visualization (Plotly):

Use Plotly for visualizing the training process and the performance of different defenses.
Plot metrics such as cumulative rewards, episode lengths, success rates of defenses, and the progression of policy updates over time.
By the end of the project, we expect to have a reinforcement learning model capable of autonomously selecting the most effective defense for each attack scenario in the image classification task. Visualization will help us monitor and analyze the agent's learning process.