Advanced AGI Model

[Deadsg]

ort os import discord from discord.ext import commands import openai import gym import numpy as np from sklearn.linear_model import LinearRegression from sklearn.ensemble import IsolationForest from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import SVC

Set up your OpenAI API key

openai.api_key = ""

Initialize the Discord bot

intents = discord.Intents.default() intents.typing = False intents.presences = False bot = commands.Bot(command_prefix='!', intents=intents)

Initialize Gym environment and create a simple Q-learning agent

env = gym.make('CartPole-v1') Q = np.zeros((env.observation_space.shape[0], env.action_space.n))

Define privileged_users (replace with actual user IDs)

privileged_users = ["Deadsg", "user_id_2"]

@bot.event async def on_ready(): print(f"We have logged in as {bot.user}")

@bot.event async def on_message(message): if message.author == bot.user: return

if message.content.startswith('!train_model'):
    # Train a simple Linear Regression model
    X = np.array([[1], [2], [3], [4]])
    y = np.array([3, 4, 2, 5])
    model = LinearRegression()
    model.fit(X, y)
    await message.channel.send(f'Model trained. Coefficient: {model.coef_}, Intercept: {model.intercept_}')

if message.content.startswith('!run_gym'):
    # Run a simple Q-learning agent in a Gym environment
    state = env.reset()
    done = False
    total_reward = 0

    while not done:
        action = np.argmax(Q[state])
        next_state, reward, done, _ = env.step(action)
        total_reward += reward
        state = next_state

    await message.channel.send(f'Total reward: {total_reward}')

if message.content.startswith("!hello"):
    # Respond with a greeting
    await message.channel.send("Hello! I'm here to demonstrate OpenAI Gym and scikit-learn.")

    # Use gym to interact with the environment (example)
    total_reward = 0
    done = False
    state = env.reset()

    while not done:
        action = env.action_space.sample()  # Random action for demonstration
        state, reward, done, _ = env.step(action)
        total_reward += reward

    result_text = f'Total Reward: {total_reward}'

    # Send the result back to the Discord channel
    await message.channel.send(result_text)

if message.content.startswith("!privileged"):
    if str(message.author.id) in privileged_users:
        # Respond to privileged user
        await message.channel.send("You are a privileged user. Here is a result for you:")

        # Use gym to interact with the environment (example)
        total_reward = 0
        done = False
        state = env.reset()

        while not done:
            action = env.action_space.sample()  # Random action for demonstration
            state, reward, done, _ = env.step(action)
            total_reward += reward

        result_text = f'Total Reward: {total_reward}'

        # Send the result back to the Discord channel
        await message.channel.send(result_text)
    else:
        await message.channel.send("You do not have permission to use this command.")

Create synthetic data for demonstration (replace with real data)

X = np.random.randn(100, 2)

Create an Isolation Forest model

clf = IsolationForest(contamination=0.1)

Fit the model

clf.fit(X)

@bot.event async def on_ready(): print(f'We have logged in as {bot.user}')

@bot.event async def on_message(message): if message.author == bot.user: return

if message.content.startswith("!cybersecurity"):
    # Use scikit-learn and Gym for a cybersecurity task (example)
    anomalies = clf.predict(X)
    num_anomalies = np.sum(anomalies == -1)

    result_text = f'Number of anomalies detected: {num_anomalies}'

    # Send the result back to the Discord channel
    await message.channel.send(result_text)

@bot.event async def on_ready(): print(f'We have logged in as {bot.user}')

@bot.event async def on_message(message): if message.author == bot.user: return

if message.content.startswith("!privileged"):
    if str(message.author.id) in privileged_users:
        # Respond to privileged user
        await message.channel.send("You are a privileged user. Here is a special message for you:")

        # Use Gym and scikit-learn to perform a task (example)
        total_reward = 0
        done = False
        state = env.reset()

        while not done:
            action = 1 if model.predict([[state[0]]])[0] > 0 else 0
            state, _, done, _ = env.step(action)
            total_reward += 1

        result_text = f'Total Reward in CartPole: {total_reward}'

        # Send the result back to the Discord channel
        await message.channel.send(result_text)
    else:
        await message.channel.send("You do not have permission to use this command.")

Initialize Isolation Forest model

clf = IsolationForest(contamination=0.1)

@bot.event async def on_ready(): print(f'We have logged in as {bot.user}')

@bot.event async def on_message(message): if message.author == bot.user: return

if message.content.startswith("!cybersecurity"):
    # Use Isolation Forest for anomaly detection (example)
    X = np.random.randn(100, 2)  # Replace with actual data
    clf.fit(X)
    anomalies = clf.predict(X)
    num_anomalies = np.sum(anomalies == -1)

    result_text = f'Number of anomalies detected: {num_anomalies}'

    # Send the result back to the Discord channel
    await message.channel.send(result_text)

Create a custom OpenAI Gym environment for a cybersecurity task

Define states, actions, rewards, and the transition dynamics

class CustomCyberEnv(gym.Env): def init(self): super(CustomCyberEnv, self).init() self.observation_space = gym.spaces.Discrete(4) # Define number of states (4 for example) self.action_space = gym.spaces.Discrete(2) # Define number of actions (2 for example) self.state = 0

def reset(self):
    self.state = 0
    return self.state

def step(self, action):
    if self.state == 0:
        if action == 0:  # Safe action
            self.state = 1
            reward = 1
        else:  # Risky action
            self.state = 2
            reward = -1
    elif self.state == 1:
        if action == 0:  # Safe action
            self.state = 3
            reward = 2
        else:  # Risky action
            self.state = 2
            reward = -1
    elif self.state == 2:
        if action == 0:  # Safe action
            self.state = 0
            reward = 1
        else:  # Risky action
            self.state = 2
            reward = -1
    elif self.state == 3:
        if action == 0:  # Safe action
            self.state = 3
            reward = 2
        else:  # Risky action
            self.state = 2
            reward = -1

    done = self.state == 2
    return self.state, reward, done, {}

env = CustomCyberEnv()

Q-learning parameters

num_episodes = 1000 learning_rate = 0.1 discount_factor = 0.9 exploration_prob = 0.1

Initialize Q-table

num_states = env.observation_space.n num_actions = env.action_space.n Q = np.zeros((num_states, num_actions))

for episode in range(num_episodes): state = env.reset() done = False

while not done:
    # Choose an action with epsilon-greedy policy
    if np.random.rand() < exploration_prob:
        action = env.action_space.sample()  # Explore
    else:
        action = np.argmax(Q[state, :])  # Exploit

    # Take the chosen action and observe the next state and reward
    next_state, reward, done, _ = env.step(action)

    # Q-learning update rule
    Q[state, action] = Q[state, action] + learning_rate * (reward + discount_factor * np.max(Q[next_state, :]) - Q[state, action])

    state = next_state

The Q-table now contains learned Q-values for actions in each state

@bot.event async def on_ready(): print(f'We have logged in as {bot.user}')

@bot.event async def on_message(message): if message.author == bot.user: return

if message.content.startswith("!cybersecurity"):
    # Use Q-learning for the cybersecurity task (example)
    state = env.reset()
    done = False
    total_reward = 0

    while not done:
        action = np.argmax(Q[state])
        next_state, reward, done, _ = env.step(action)
        total_reward += reward
        state = next_state

    result_text = f'Total Reward in Cybersecurity Task: {total_reward}'

    # Send the result back to the Discord channel
    await message.channel.send(result_text)

    # Define a custom OpenAI Gym environment for a reinforcement learning task

Define states, actions, rewards, and the transition dynamics

class CustomEnv(gym.Env): def init(self): super(CustomEnv, self).init() self.observation_space = gym.spaces.Discrete(5) # Define number of states (5 for example) self.action_space = gym.spaces.Discrete(2) # Define number of actions (2 for example) self.state = 0

def reset(self):
    self.state = 0
    return self.state

def step(self, action):
    # Define your transition dynamics and rewards based on the chosen action and current state
    if self.state == 0:
        if action == 0:
            self.state = 1
            reward = 1
        else:
            self.state = 2
            reward = -1
    elif self.state == 1:
        if action == 0:
            self.state = 3
            reward = 2
        else:
            self.state = 2
            reward = -1
    elif self.state == 2:
        if action == 0:
            self.state = 0
            reward = 1
        else:
            self.state = 2
            reward = -1
    elif self.state == 3:
        if action == 0:
            self.state = 3
            reward = 2
        else:
            self.state = 2
            reward = -1
    elif self.state == 4:
        self.state = 0
        reward = 10
    done = self.state == 4
    return self.state, reward, done, {}

env = CustomEnv()

Q-learning parameters

num_episodes = 1000 learning_rate = 0.1 discount_factor = 0.9 exploration_prob = 0.1

Initialize Q-table

num_states = env.observation_space.n num_actions = env.action_space.n Q = np.zeros((num_states, num_actions))

for episode in range(num_episodes): state = env.reset() done = False

while not done:
    # Choose an action with epsilon-greedy policy
    if np.random.rand() < exploration_prob:
        action = env.action_space.sample()  # Explore
    else:
        action = np.argmax(Q[state, :])  # Exploit

    # Take the chosen action and observe the next state and reward
    next_state, reward, done, _ = env.step(action)

    # Q-learning update rule
    Q[state, action] = Q[state, action] + learning_rate * (reward + discount_factor * np.max(Q[next_state, :]) - Q[state, action])

    state = next_state

The Q-table now contains learned Q-values for actions in each state

@bot.event async def on_message(message): if message.author == bot.user: return

if message.content.startswith('!ask_gpt3'):
    # Define a prompt to send to ChatGPT
    prompt = "Translate the following English text to French:"

    # Extract user query from message
    user_query = message.content[len('!ask_gpt3 '):]

    # Combine prompt and user query
    full_prompt = f"{prompt} {user_query}"

    # Use the model to get a response from ChatGPT
    response = openai.Completion.create(
      engine="davinci",  # Choose your preferred model here
      prompt=full_prompt,
      max_tokens=50  # Adjust as needed
    )

    # Extract the answer from the response
    answer = response.choices[0].text.strip()

    # Send the answer back to the Discord channel
    await message.channel.send(answer)        

Run the bot

bot.run('') # Replace with your bot token

[jmikedupont2]

ok i see what you are doing. may i suggest you split this up ito differnt bots for different task and make the tasks generically run?

[Deadsg]

See boss the key is getting it all running in one long string. Thats what original batsy was. I have multiple bots. and assined directories. Im just taking care of batsy rn. Which is a large task for a beginner

[Deadsg]

I think so atleast. I tried a shorter string and it was non functional

[jmikedupont2]

I suggest make many tasks that the bot can use and those tasks are defined and tested outside the bot. this is what we are doing for introspector

On Sat, Sep 9, 2023, 07:06 Deadsg @.***> wrote:

I think so atleast. I tried a shorter string and it was non functional

— Reply to this email directly, view it on GitHub https://github.com/Deadsg/BatsyDefenseAi/issues/7#issuecomment-1712485509, or unsubscribe https://github.com/notifications/unsubscribe-auth/AD5KQ2LSQW63CDJGNZ3SGYTXZRETNANCNFSM6AAAAAA4RLXUZU . You are receiving this because you commented.Message ID: @.***>