Simple AGI Agent Model

import numpy as np

class SimpleAGIAgent:
    def __init__(self, states, actions):
        self.states = states  # A list of possible states
        self.actions = actions  # A list of possible actions
        self.q_table = np.zeros((states, actions))  # Q-Value table
        self.learning_rate = 0.1  # How quickly the agent updates its knowledge
        self.discount_rate = 0.95  # Importance of future rewards
        self.exploration_rate = 1.0  # Initial exploration rate
        self.exploration_decay = 0.01  # Decay rate for exploration
        self.min_exploration_rate = 0.01  # Minimum exploration rate

    def choose_action(self, state):
        # Decide whether to explore or exploit
        if np.random.rand() < self.exploration_rate:
            action = np.random.choice(self.actions)
        else:
            action = np.argmax(self.q_table[state])
        return action

    def update_q_table(self, state, action, reward, next_state):
        # Update Q-Value table using the Q-learning algorithm
        next_max = np.max(self.q_table[next_state])
        q_value = self.q_table[state, action]
        new_q_value = (1 - self.learning_rate) * q_value + self.learning_rate * (reward + self.discount_rate * next_max)
        self.q_table[state, action] = new_q_value

        # Decay exploration rate
        self.exploration_rate = max(self.min_exploration_rate, self.exploration_rate - self.exploration_decay)

# Example usage
states = 10  # For simplicity, assume 10 possible states
actions = 2  # Assume 2 possible actions

agent = SimpleAGIAgent(states, actions)

# Simulate agent's decision making and learning in an environment
for episode in range(100):  # Simulate 100 episodes
    state = np.random.randint(0, states)  # Random initial state
    done = False  # To simulate the end of an episode
    
    while not done:
        action = agent.choose_action(state)
        next_state = (state + 1) % states  # Simplistic environment response
        reward = 1 if next_state == 0 else -1  # Reward for reaching a specific state
        agent.update_q_table(state, action, reward, next_state)
        
        state = next_state
        done = state == 0  # Episode ends when reaching state 0

    print(f"Episode {episode + 1}, Exploration rate: {agent.exploration_rate}")

# Print the learned Q-values
print("Learned Q-Values:")
print(agent.q_table)