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import pyboy import gym import numpy as np import pickle import multiprocessing from pyboy import PyBoy, WindowEvent from gym import spaces # Custom Gym environment for Pokemon class PokemonEnv(gym.Env): def __init__(self, pyboy_instance): self.pyboy = pyboy_instance # Additional RAM locations self.pokemon_in_party_addr = 0xD163 self.pokemon_data_start_addr = 0xD164 self.level_addr_offset = 0xD18C self.current_hp_offset = 0xD16C self.gym_badges_addr = 0xD755 # Initialize reward variables self.total_level = 0 self.total_healing = 0 self.step_count = 0 self.max_steps = 1000 # Adjust as needed self.max_badges_reward = 0 # Define a simple discrete action space self.action_space = spaces.Discrete(8) # Adjust the size based on your actions def read_byte(self, address): return self.pyboy.get_memory_value(address) def read_word(self, address): return self.pyboy.get_memory_value(address) + (self.pyboy.get_memory_value(address + 1) << 8) def get_pokemon_data(self, pokemon_addr): pokemon_data = { 'pokemon_id': self.read_byte(pokemon_addr), 'current_hp': self.read_word(pokemon_addr + self.current_hp_offset), 'level': self.read_byte(pokemon_addr + self.level_addr_offset) } return pokemon_data def step(self, action): # Take action in the emulator self.pyboy.tick() state = self.pyboy.botsupport_manager().screen() # Compute the reward and check if the episode is done reward = 0.0 done = False # Retrieve information from RAM locations pokemon_in_party = self.read_byte(self.pokemon_in_party_addr) gym_badges = self.read_byte(self.gym_badges_addr) # Iterate over Pokémon in the party for i in range(pokemon_in_party): pokemon_data_addr = self.pokemon_data_start_addr + i * 7 pokemon_data = self.get_pokemon_data(pokemon_data_addr) # Update reward variables self.total_level += pokemon_data['level'] self.total_healing += pokemon_data['current_hp'] # Example: Reward based on level reward += self.total_level_reward * pokemon_data['level'] # Example: Reward for gym badges reward += self.max_badges_reward * gym_badges # Update step count self.step_count += 1 # Apply penalties for long episodes if self.step_count > self.max_steps: done = True return state, reward, done, {} # Your AI agent class class PokemonAgent: def __init__(self, action_space): self.action_space = action_space self.valid_actions = [ WindowEvent.PRESS_ARROW_DOWN, WindowEvent.PRESS_ARROW_LEFT, WindowEvent.PRESS_ARROW_RIGHT, WindowEvent.PRESS_ARROW_UP, WindowEvent.PRESS_BUTTON_A, WindowEvent.PRESS_BUTTON_B, WindowEvent.PRESS_BUTTON_START, WindowEvent.PASS ] self.release_arrow = [ WindowEvent.RELEASE_ARROW_DOWN, WindowEvent.RELEASE_ARROW_LEFT, WindowEvent.RELEASE_ARROW_RIGHT, WindowEvent.RELEASE_ARROW_UP ] self.release_button = [ WindowEvent.RELEASE_BUTTON_A, WindowEvent.RELEASE_BUTTON_B ] self.q_values = {} # Q-values for state-action pairs, initialize as needed def choose_action(self, state): # Random action for testing chosen_action = np.random.choice(self.valid_actions) return chosen_action def update_q_values(self, state, action, reward, next_state): # Q-learning update learning_rate = 0.1 discount_factor = 0.9 # Get the current Q-value for the (state, action) pair current_q_value = self.q_values.get((state, action), 0.0) # Estimate the future Q-value using the maximum Q-value of the next state max_next_q_value = max(self.q_values.get((next_state, a), 0.0) for a in self.valid_actions) # Update the Q-value based on the Q-learning formula updated_q_value = current_q_value + learning_rate * (reward + discount_factor * max_next_q_value - current_q_value) # Update the Q-value in the Q-values dictionary self.q_values[(state, action)] = updated_q_value pass def save_q_values(self, filename): # Save Q-values to a file using pickle with open(filename, 'wb') as file: pickle.dump(self.q_values, file) def load_q_values(self, filename): # Load Q-values from a file using pickle with open(filename, 'rb') as file: self.q_values = pickle.load(file) def worker(episode, agent): pyboy_instance = PyBoy("PokemonRed.gb", window_type="SDL2", window_scale=2) env = PokemonEnv(pyboy_instance) state = env.reset() total_reward = 0.0 while True: action = agent.choose_action(state) if action in agent.release_arrow: env.pyboy.send_input(WindowEvent.RELEASE_ALL) env.pyboy.send_input(action) elif action in agent.release_button: env.pyboy.send_input(action) env.pyboy.send_input(WindowEvent.RELEASE_ALL) else: env.pyboy.send_input(action) next_state, reward, done, _ = env.step(action) # Implement your learning and updating logic here total_reward += reward state = next_state # Render the environment env.pyboy.tick() env.pyboy.tick() env.pyboy.tick() env.render() if done: print(f"Episode {episode + 1}, Total Reward: {total_reward}") # Save Q-values and model after each episode agent.save_q_values(f'q_values_episode_{episode + 1}.pkl') agent.save_model(f'model_episode_{episode + 1}.pkl') break if __name__ == "__main__": action_space_size = 8 # Adjust this based on your actual action space size agent = PokemonAgent(spaces.Discrete(action_space_size))