Define Code Structure

[PaulZbigniew]

For more advanced AI algorithms, it's often better to keep the game environment and the AI agent separate. This separation allows for more flexibility and easier integration of different agents.

Here's a recommended structure for the implementation, including:

• game environment
• agent
• policy
• interactions

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Game Environment (UTTT):

Create a UTTT class that encapsulates the game rules, state representation, and methods for interacting with the game. This class should include functions to:

• Initialize the game state.
• Determine valid actions.
• Update the game state based on selected actions.
• Check for game termination and outcomes.

class UTTT:
    def __init__(self):
        # Initialize game state, rules, and attributes
        ...

    def get_current_state(self):
        # Return the current game state
        ...

    def get_valid_actions(self):
        # Return a list of valid actions in the current state
        ...

    def perform_action(self, action):
        # Update the game state based on the selected action
        ...

    def is_game_over(self):
        # Check if the game is over and determine the outcome
        ...

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Agent:

Create an agent class that can interact with the game environment to make decisions. You can implement different agents, including the random baseline agent, MCTS-based agent, or more advanced AI models.

class RandomAgent:
    def __init__(self, num_actions):
        # Initialize the agent with relevant parameters
        ...

    def select_action(self, state):
        # Implement action selection logic (e.g., random choice)
        ...

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Policy:

If you plan to implement more advanced algorithms, such as MCTS or deep reinforcement learning, you might use a policy class to represent the agent's strategy.

class Policy:
    def __init__(self, model):
        # Initialize the policy with a model (e.g., neural network)
        ...

    def select_action(self, state):
        # Implement action selection logic based on the policy model
        ...

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Interactions and Game Loop:

Finally, in your main script or game loop, you can set up the game environment, instantiate the agent, and manage interactions. The game loop should involve the following steps:

# Initialize the game environment
game = UTTT()

# Initialize the agent
agent = RandomAgent(game.get_num_actions())  # Or use a more advanced agent if desired

while not game.is_game_over():
    # Get the current state from the game
    state = game.get_current_state()

    # The agent selects an action based on the state
    action = agent.select_action(state)

    # Update the game state based on the action
    game.perform_action(action)

# Determine the outcome and handle it accordingly
outcome = game.get_game_outcome()

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