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.


TASK : 
Objective:
To implement an advanced reinforcement learning (RL) algorithm using an existing Python RL library/module to train an autonomous robot (or agent) to navigate through a complex environment with dynamic obstacles. The robot's goal is to reach a designated target location while avoiding obstacles and minimizing energy consumption. A GUI for real-time visualization of the agent’s behavior will be built using Plotly, allowing detailed monitoring of the agent's learning process, including states, actions, rewards, and policy evolution.

Key Components:
Environment:

The environment is a 2D grid-like world (e.g., maze or city layout) with randomly placed dynamic obstacles.
The agent (robot) starts at a random location and must navigate to a fixed goal point.
Obstacles in the environment may move unpredictably, making the task more challenging.
The environment will provide state information (such as position, distance from the goal, proximity to obstacles) to the agent.
Agent:

The agent will be equipped with sensors (in the form of state information) that detect the environment’s status (e.g., nearby obstacles, distance to the target).
The agent will decide its next move (up, down, left, right) based on the reinforcement learning algorithm.
The agent should optimize its policy to reach the goal as quickly and efficiently as possible while avoiding collisions with obstacles and minimizing unnecessary movements.
Reinforcement Learning Algorithm:

The agent will be trained using a suitable RL algorithm (e.g., DQN, PPO, A3C) provided by an existing Python library/module (such as Stable-Baselines3, TensorFlow Agents, or PyTorch RL libraries).
The algorithm will aim to maximize the cumulative reward over time, where rewards are defined as follows:
Positive reward for moving closer to the goal.
Negative reward for hitting obstacles or taking inefficient actions.
Reward decay based on time to encourage faster navigation.
Visualization using Plotly:

A dynamic GUI using Plotly will be developed to visualize the agent’s interaction with the environment in real-time.
The GUI will include:
A 2D grid showing the agent’s current position, the goal, and obstacles.
A live plot of the agent’s movement across the grid.
Graphical representations of the agent’s reward, state transitions, and learning metrics (such as loss and policy updates) over time.
The visualization will allow researchers to observe how the agent improves its policy through trial and error.
Additional Challenges:
Exploration vs Exploitation: Ensure that the agent balances exploring new paths with exploiting known optimal routes.
Dynamic Obstacles: The agent needs to adapt to the changing positions of obstacles in the environment.
Energy Efficiency: Minimize the number of moves the agent makes, representing efficient energy consumption for real-world robot applications.
Tools and Technologies:
Python: Core programming language.
RL Libraries: Stable-Baselines3, PyTorch, or TensorFlow Agents for implementing RL algorithms.
Plotly: For creating the GUI to visualize the agent’s behavior and learning process in real-time.
Success Criteria:
The agent successfully learns to navigate to the target while avoiding obstacles.
The GUI accurately reflects the agent’s state and decision-making process in real time.
The agent's performance improves over time, as shown by an increasing cumulative reward and more efficient navigation.