ruvnet · March 5, 2026 17:13
diff --git a/q.py b/q.py
 #             - Q* (Q-Star)
 #    /\__/\   - q.py
 #   ( o.o  )  - v0.0.1
 #     >^<     - by @rUv

 # 01110010 01110101 01110110
 # This is a proof of concept implementation of the Q* (AGI) leak from OpenAi
 # This Python code defines a sophisticated Q-learning agent for reinforcement learning. 
 # It includes dynamic exploration, learning from experiences, and checks for convergence. 
 # The agent's capabilities are refined iteratively to optimize its decision-making strategy in a given environment.


 import numpy as np
 import random

 class SophisticatedQLearningAgent:
    def __init__(self, states, actions, learning_rate=0.1, discount_factor=0.95, exploration_rate=1.0, min_exploration_rate=0.01, exploration_decay_rate=0.995, max_episodes=10000, max_steps_per_episode=200):
        # Initialize the Q-learning agent with specified parameters.
        self.states = states  # Number of states in the environment
        self.actions = actions  # Number of possible actions
        self.learning_rate = learning_rate  # Rate at which the agent learns
        self.discount_factor = discount_factor  # Factor for discounting future rewards
        self.exploration_rate = exploration_rate  # Initial exploration rate
        self.min_exploration_rate = min_exploration_rate  # Minimum exploration rate
        self.exploration_decay_rate = exploration_decay_rate  # Rate of decay for exploration
        self.max_episodes = max_episodes  # Maximum number of training episodes
        self.max_steps_per_episode = max_steps_per_episode  # Maximum steps per episode
        self.q_table = np.zeros((states, actions))  # Initialize Q-table with zeros

    def choose_action(self, state):
        # Choose an action based on the current state and exploration-exploitation strategy.
        if random.uniform(0, 1) < self.exploration_rate:
            action = random.randint(0, self.actions - 1)  # Explore: choose a random action
        else:
            action = np.argmax(self.q_table[state, :])  # Exploit: choose the best known action
        return action

    def learn(self, state, action, reward, next_state):
        # Update the Q-table based on the action taken and the resulting state.
        predict = self.q_table[state, action]
        target = reward + self.discount_factor * np.max(self.q_table[next_state, :])
        self.q_table[state, action] += self.learning_rate * (target - predict)

    def update_exploration_rate(self):
        # Decrease exploration rate over time.
        self.exploration_rate = max(self.min_exploration_rate, self.exploration_rate * self.exploration_decay_rate)

    def has_converged(self, threshold=0.005):
        # Check if the Q-values have converged.
        return np.all(np.abs(self.q_table - np.max(self.q_table, axis=1, keepdims=True)) < threshold)

    def train(self):
        # Train the agent over a series of episodes.
        for episode in range(self.max_episodes):
            state = random.randint(0, self.states - 1)
            for step in range(self.max_steps_per_episode):
                action = self.choose_action(state)
                next_state = random.randint(0, self.states - 1)
                reward = 1 if next_state == self.states - 1 else 0
                self.learn(state, action, reward, next_state)
                state = next_state
            self.update_exploration_rate()
            if self.has_converged():
                return True, episode
        return False, self.max_episodes

    def resize_q_table(self, new_states, new_actions):
        # Resize the Q-table if the number of states or actions increases.
        if new_states > self.states or new_actions > self.actions:
            new_q_table = np.zeros((new_states, new_actions))
            new_q_table[:self.states, :self.actions] = self.q_table
            self.q_table = new_q_table
            self.states = new_states
            self.actions = new_actions

 # Initialize and refine the sophisticated agent
 sophisticated_agent = SophisticatedQLearningAgent(states=30, actions=4)
 max_refinements = 5
 refinement_count = 0
 converged = False

 # Refine the agent until it converges or reaches the maximum number of refinements
 while not converged and refinement_count < max_refinements:
    new_states = sophisticated_agent.states + 10  # Increasing the number of states
    new_actions = sophisticated_agent.actions  # Keeping the number of actions constant
    sophisticated_agent.resize_q_table(new_states, new_actions)  # Resize the Q-table
    sophisticated_agent.max_episodes += 5000  # Increase the number of episodes
    sophisticated_agent.max_steps_per_episode += 50  # Increase the steps per episode
    converged, episodes = sophisticated_agent.train()  # Train the agent
    refinement_count += 1

 # Result of training after refinements
 print(f"Converged: {converged}, Episodes: {episodes}, Refinements: {refinement_count}")
 print(f"Q-Table:\n{sophisticated_agent.q_table}")
	# - Q* (Q-Star)
	# /\__/\ - q.py
	# ( o.o ) - v0.0.1
	# >^< - by @rUv

	# 01110010 01110101 01110110
	# This is a proof of concept implementation of the Q* (AGI) leak from OpenAi
	# This Python code defines a sophisticated Q-learning agent for reinforcement learning.
	# It includes dynamic exploration, learning from experiences, and checks for convergence.
	# The agent's capabilities are refined iteratively to optimize its decision-making strategy in a given environment.


	import numpy as np
	import random

	class SophisticatedQLearningAgent:
	def __init__(self, states, actions, learning_rate=0.1, discount_factor=0.95, exploration_rate=1.0, min_exploration_rate=0.01, exploration_decay_rate=0.995, max_episodes=10000, max_steps_per_episode=200):
	# Initialize the Q-learning agent with specified parameters.
	self.states = states # Number of states in the environment
	self.actions = actions # Number of possible actions
	self.learning_rate = learning_rate # Rate at which the agent learns
	self.discount_factor = discount_factor # Factor for discounting future rewards
	self.exploration_rate = exploration_rate # Initial exploration rate
	self.min_exploration_rate = min_exploration_rate # Minimum exploration rate
	self.exploration_decay_rate = exploration_decay_rate # Rate of decay for exploration
	self.max_episodes = max_episodes # Maximum number of training episodes
	self.max_steps_per_episode = max_steps_per_episode # Maximum steps per episode
	self.q_table = np.zeros((states, actions)) # Initialize Q-table with zeros

	def choose_action(self, state):
	# Choose an action based on the current state and exploration-exploitation strategy.
	if random.uniform(0, 1) < self.exploration_rate:
	action = random.randint(0, self.actions - 1) # Explore: choose a random action
	else:
	action = np.argmax(self.q_table[state, :]) # Exploit: choose the best known action
	return action

	def learn(self, state, action, reward, next_state):
	# Update the Q-table based on the action taken and the resulting state.
	predict = self.q_table[state, action]
	target = reward + self.discount_factor * np.max(self.q_table[next_state, :])
	self.q_table[state, action] += self.learning_rate * (target - predict)

	def update_exploration_rate(self):
	# Decrease exploration rate over time.
	self.exploration_rate = max(self.min_exploration_rate, self.exploration_rate * self.exploration_decay_rate)

	def has_converged(self, threshold=0.005):
	# Check if the Q-values have converged.
	return np.all(np.abs(self.q_table - np.max(self.q_table, axis=1, keepdims=True)) < threshold)

	def train(self):
	# Train the agent over a series of episodes.
	for episode in range(self.max_episodes):
	state = random.randint(0, self.states - 1)
	for step in range(self.max_steps_per_episode):
	action = self.choose_action(state)
	next_state = random.randint(0, self.states - 1)
	reward = 1 if next_state == self.states - 1 else 0
	self.learn(state, action, reward, next_state)
	state = next_state
	self.update_exploration_rate()
	if self.has_converged():
	return True, episode
	return False, self.max_episodes

	def resize_q_table(self, new_states, new_actions):
	# Resize the Q-table if the number of states or actions increases.
	if new_states > self.states or new_actions > self.actions:
	new_q_table = np.zeros((new_states, new_actions))
	new_q_table[:self.states, :self.actions] = self.q_table
	self.q_table = new_q_table
	self.states = new_states
	self.actions = new_actions

	# Initialize and refine the sophisticated agent
	sophisticated_agent = SophisticatedQLearningAgent(states=30, actions=4)
	max_refinements = 5
	refinement_count = 0
	converged = False

	# Refine the agent until it converges or reaches the maximum number of refinements
	while not converged and refinement_count < max_refinements:
	new_states = sophisticated_agent.states + 10 # Increasing the number of states
	new_actions = sophisticated_agent.actions # Keeping the number of actions constant
	sophisticated_agent.resize_q_table(new_states, new_actions) # Resize the Q-table
	sophisticated_agent.max_episodes += 5000 # Increase the number of episodes
	sophisticated_agent.max_steps_per_episode += 50 # Increase the steps per episode
	converged, episodes = sophisticated_agent.train() # Train the agent
	refinement_count += 1

	# Result of training after refinements
	print(f"Converged: {converged}, Episodes: {episodes}, Refinements: {refinement_count}")
	print(f"Q-Table:\n{sophisticated_agent.q_table}")
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