We want to train an AI system to play a simple game of Pong, a two-player table tennis game, using reinforcement learning.

To develop an AI system that can learn to play Pong through trial and error, and eventually beat human players at the game.

• Develop a functional AI system that can play Pong
• Optimize the AI system to improve its performance over time
• Provide a user interface that allows human players to interact with the AI system
• Deploy the system to a cloud server for remote access

1. Develop a prototype AI system that can play Pong (Size: Large) High level requirements: Implement a deep reinforcement learning algorithm to learn how to play Pong, train the algorithm on a dataset of Pong games, and test the system's performance against human players
2. Evaluate and optimize the AI system's performance (Size: Medium) High level requirements: Analyze the system's performance data to identify areas for improvement, make adjustments to the system's hyperparameters and neural network architecture, and retrain the system on a larger dataset of Pong games
3. Develop a user interface for the AI system (Size: Small) High level requirements: Design a user interface that allows human players to select game options and interact with the AI system, integrate the interface with the AI system's game engine, and provide feedback to users on the system's performance
4. Deploy the AI system to a cloud server (Size: Medium) High level requirements: Configure and deploy the AI system to a cloud server, ensure that the system can handle high levels of traffic and maintain uptime, and implement monitoring and logging tools to track the system's performance and identify issues as they arise.

1. Develop a game emulator in Pygame that can simulate games of Pong with two paddles and a ball.
2. Create an interface for the game emulator that allows the reinforcement learning algorithm to input game states and receive game actions.
3. Develop a deep reinforcement learning algorithm that can learn to play Pong using input from a game emulator.
4. Train the algorithm on a dataset of Pong games, using techniques such as Q-learning or policy gradient methods.
5. Implement a game engine that can simulate games of Pong, and integrate the reinforcement learning algorithm with the game engine.
6. Develop a testing framework to evaluate the system's performance against human players or other AI systems, and use this framework to iteratively improve the system's performance.
7. Optimize the algorithm's hyperparameters and neural network architecture to improve its performance and reduce training time.
8. Write clean, well-documented code that adheres to industry best practices and is easy to maintain and extend.

Initialize Q(s, a) arbitrarily
Initialize environment and agent
Set hyperparameters:
    learning_rate
    discount_factor
    exploration_rate
    exploration_decay_rate
For each episode:
    Reset the environment
    Observe the initial state s
    Set done to False
    While not done:
        With probability ε select a random action a, otherwise select a = argmaxQ(s, a)
        Take action a and observe the reward r and the next state s'
        Update Q(s, a) using the Q-learning update:
            Q(s, a) ← Q(s, a) + learning_rate * (r + discount_factor * maxQ(s', a') - Q(s, a))
        Update the state to s'
        If the game is over, set done to True
    Update the exploration rate:
        ε ← ε * exploration_decay_rate