Add AI to learn patterns

[JeffersonRPM]

First of all, I would like to congratulate you, Adria, on your excellent work! Would it be possible to implement an AI that can learn patterns, instead of just making random moves? One suggestion would be to provide an option so that we, as users, can train the code to make the best choices, since the card mapping is working so well. We could, for example, use the 'solo battle' option, with the 'auto' option enabled, allowing the AI ​​to learn from the Pokémon's own AI logic.

[JeffersonRPM]

I made a video teaching how to use your Pokemon bot project, it's in Portuguese.

https://www.youtube.com/watch?v=Nt7a-EwdwGk

[JeffersonRPM]

Suggestion:

Adding AI to the Game to Learn from Your Play Patterns

To integrate AI into your project and have it learn from your play patterns, we can focus on two main strategies: Reinforcement Learning (RL) and Supervised Learning. Here's a structured guide on how you can proceed:

1. Data Collection: Logging Your Moves

To allow the AI to learn, you'll need to collect data about the actions performed during the game. This includes:

• Actions taken by the bot (e.g., choosing a Pokémon, using an attack, etc.).
• Game state at the moment of the move (e.g., player stats, cards in hand, etc.).
• Outcome of the action (e.g., win, damage dealt, exploited weakness).

You can store these actions in a structured format, such as JSON:

game_data = {
    "moves": [
        {
            "player_action": "attack",  # Bot action
            "card_used": "A1-001",      # Card used
            "opponent_action": "defend",
            "outcome": "win",           # Outcome
            "context": {
                "hand": ["A1-001", "A1-002", "A1-003"],
                "opponent_health": 50,
                "player_health": 60,
            }
        },
        # Other moves
    ]
}

2. Choosing the AI Model

You can choose between Reinforcement Learning (RL) and Supervised Learning:

a) Reinforcement Learning (RL)

RL is a great option where the bot learns through rewards and penalties based on its actions in the game. The bot will try to maximize its reward over time by adjusting its strategy.

You can use libraries like stable-baselines3 to implement RL:

pip install stable-baselines3

Then, create a custom game environment where the bot can learn and take actions:

from stable_baselines3 import PPO
import gym

# Define the game environment
class PokemonGameEnv(gym.Env):
    def __init__(self):
        super(PokemonGameEnv, self).__init__()
        self.action_space = gym.spaces.Discrete(10)  # Example: 10 possible actions
        self.observation_space = gym.spaces.Box(low=0, high=1, shape=(10,))  # Game state features

    def reset(self):
        # Reset the game state
        return self.get_observation()

    def get_observation(self):
        # Get the current game state
        return self.current_state

    def step(self, action):
        # Take action and return new state, reward, and if the game is over
        reward = self.calculate_reward(action)
        done = False  # Define when the game is over
        return self.get_observation(), reward, done, {}

    def calculate_reward(self, action):
        # Calculate the reward (e.g., damage dealt, winning)
        return 10  # Simplified reward example

# Train the model
env = PokemonGameEnv()
model = PPO("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=10000)

b) Supervised Learning

If you already have a dataset of past moves, you can use supervised learning to predict the best action based on the game state. The inputs could be the current state of the game (e.g., cards in hand, health) and the outputs would be the best action (e.g., selecting a card or attack).

You can use models like Random Forest, XGBoost, or neural networks with Keras or Scikit-learn for this.

3. Integrating the AI into the Game

Once the AI model is trained, you can integrate it into the game, replacing manual decisions with AI predictions.

Example of how the bot would make a move using the trained model:

class PokemonBot:
    def __init__(self, trained_model, app_state, log_callback, ui_instance):
        self.model = trained_model
        self.app_state = app_state
        self.log_callback = log_callback
        self.ui_instance = ui_instance

    def make_move(self, game_state):
        # Preprocess the game state for model input
        state_features = self.process_game_state(game_state)

        # Predict the best action based on the current game state
        action = self.model.predict(state_features)
        
        # Perform the action in the game
        self.perform_action(action)

    def process_game_state(self, game_state):
        # Convert the game state to a suitable format for the model
        return [game_state["health"], game_state["hand"], game_state["opponent_health"]]

    def perform_action(self, action):
        # Implement the logic to perform the action in the game
        pass

4. Monitoring and Adjusting

As the AI learns, you should monitor its performance and adjust the training strategy if necessary. Consider:

• Continuous Reinforcement Learning: The bot continues to learn as it plays more games.
• Regular Evaluation: Test the bot in different scenarios and adjust the training data as needed.

Final Thoughts:

• Exploration vs Exploitation: In RL, the bot needs to balance exploring new actions and exploiting known good actions. This can be adjusted using methods like epsilon-greedy or entropy in RL.
• Training Environment: For RL, you'll need a simulated environment where the bot can train without constant player input.
• Performance: Training AI models, especially in RL, can take time. If needed, you can speed up training with GPUs or parallel processing.

By following this process, you can integrate AI into your game that learns from your actions and becomes more intelligent over time.