separate Player class to allow for an AI player

[keithgw]

methods to modify for AI player:

• _choose_action This should follow the agent's policy
• _choose_a_bird_to_play This should follow the agent's policy
• draw_a_bird This should follow a similar pattern to play a bird, where the choosing is separate from the actual drawing.

[keithgw]

Implement a State and Policy class, for example:

class State:
    def __init__(self, game_board, phase):
        self.game_board = game_board
        self.phase = phase

class Policy:
    def __call__(self, state):
        if state.phase == 'choose_action':
            return self._policy_choose_action(state.game_board)
        elif state.phase == 'choose_bird':
            return self._policy_choose_bird(state.game_board)

    def _policy_choose_action(self, game_board):
        # Return probabilities for the actions 'play_a_bird', 'gain_food', 'draw_a_card'
        pass

    def _policy_choose_bird(self, game_board):
        # Return probabilities for the actions 'choose_from_tray', 'choose_from_deck'
        pass

Then, the bots choice methods can look like:

import numpy as np

class BotPlayer(Player):
    def __init__(self):
        super().__init__()
        self.policy = None  # Load learned policy here

    def _choose_a_bird_to_draw(self, bird_deck, tray):
        # Convert the current game state to a format that your policy can understand
        state = self._get_state(bird_deck, tray)

        # Use the policy to get the probabilities of each action
        action_probs = self.policy(state)

        # Choose an action according to the probabilities
        action = np.random.choice(len(action_probs), p=action_probs)

        # Convert the action back to a bird
        chosen_bird = self._action_to_bird(action, bird_deck, tray)

        return chosen_bird