Genesis AGI

import torch import torch.nn as nn import torch.optim as optim import numpy as np import random Define a Transformer model for reasoning with multi-modal support class AGITransformer(nn.Module): def init(self, input_dim, hidden_dim, output_dim): super(AGITransformer, self).init() self.embedding = nn.Linear(input_dim, hidden_dim) self.transformer = nn.Transformer( d_model=hidden_dim, nhead=4, num_encoder_layers=4, num_decoder_layers=4, batch_first=True # Ensure batch-first format ) self.output_layer = nn.Linear(hidden_dim, output_dim) def forward(self, x): x = self.embedding(x).unsqueeze(0) # Add batch dimension x = self.transformer(x, x) x = self.output_layer(x.squeeze(0)) # Remove batch dimension return x Memory system with prioritized experience retention class Memory: def init(self): self.store = [] def remember(self, state, action, reward): self.store.append((state, action, reward)) self.store.sort(key=lambda x: x[2], reverse=True) # Prioritize high rewards if len(self.store) > 10000: self.store.pop(-1) # Remove lowest priority experiences def retrieve(self): return random.sample(self.store, min(10, len(self.store))) if self.store else [(None, None, 0)] Goal-based reinforcement learning agent with self-optimization class AGIAgent: def init(self, input_dim, hidden_dim, output_dim): self.model = AGITransformer(input_dim, hidden_dim, output_dim) self.memory = Memory() self.optimizer = optim.Adam(self.model.parameters(), lr=0.001) self.criterion = nn.MSELoss() self.goal = None # Internal goal system def choose_action(self, state): state_tensor = torch.tensor(state, dtype=torch.float32) with torch.no_grad(): action_values = self.model(state_tensor) return torch.argmax(action_values).item() def train(self): if len(self.memory.store) < 10: return # Not enough experiences yet for state, action, reward in self.memory.retrieve(): state_tensor = torch.tensor(state, dtype=torch.float32) predicted_rewards = self.model(state_tensor) target = predicted_rewards.clone() target[action] = reward loss = self.criterion(predicted_rewards, target.detach()) self.optimizer.zero_grad() loss.backward() self.optimizer.step() def set_goal(self, new_goal): """Set a new internal goal for strategic planning.""" self.goal = new_goal print(f"New goal set: {self.goal}") def adjust_learning(self): """Meta-learning: Adjust learning rate based on recent success.""" if self.memory.store and np.mean([r[2] for r in self.memory.store[-10:]]) > 0.5: for param_group in self.optimizer.param_groups: param_group['lr'] = 1.1 # Increase learning rate if performing well elif self.memory.store: for param_group in self.optimizer.param_groups: param_group['lr'] = 0.9 # Decrease if struggling Example environment interaction if name == "main": agent = AGIAgent(input_dim=10, hidden_dim=128, output_dim=4) agent.set_goal("Maximize positive rewards while exploring efficiently.") for episode in range(1000): # Extended interaction loop for deeper learning state = np.random.rand(10) action = agent.choose_action(state) reward = np.random.rand() * (1 if action % 2 == 0 else -1) # Structured reward agent.memory.remember(state, action, reward) agent.train() agent.adjust_learning() # Optimize learning process dynamically print("Training completed. The AGI model has learned from experience.")

Mar 12, 2025 - 06:10

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import random

Define a Transformer model for reasoning with multi-modal support

class AGITransformer(nn.Module):
def init(self, input_dim, hidden_dim, output_dim):
super(AGITransformer, self).init()
self.embedding = nn.Linear(input_dim, hidden_dim)
self.transformer = nn.Transformer(
d_model=hidden_dim,
nhead=4,
num_encoder_layers=4,
num_decoder_layers=4,
batch_first=True # Ensure batch-first format
)
self.output_layer = nn.Linear(hidden_dim, output_dim)

def forward(self, x):
    x = self.embedding(x).unsqueeze(0)  # Add batch dimension
    x = self.transformer(x, x)
    x = self.output_layer(x.squeeze(0))  # Remove batch dimension
    return x

Memory system with prioritized experience retention

class Memory:
def init(self):
self.store = []

def remember(self, state, action, reward):
    self.store.append((state, action, reward))
    self.store.sort(key=lambda x: x[2], reverse=True)  # Prioritize high rewards
    if len(self.store) > 10000:
        self.store.pop(-1)  # Remove lowest priority experiences

def retrieve(self):
    return random.sample(self.store, min(10, len(self.store))) if self.store else [(None, None, 0)]

Goal-based reinforcement learning agent with self-optimization

class AGIAgent:
def init(self, input_dim, hidden_dim, output_dim):
self.model = AGITransformer(input_dim, hidden_dim, output_dim)
self.memory = Memory()
self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)
self.criterion = nn.MSELoss()
self.goal = None # Internal goal system

def choose_action(self, state):
    state_tensor = torch.tensor(state, dtype=torch.float32)
    with torch.no_grad():
        action_values = self.model(state_tensor)
    return torch.argmax(action_values).item()

def train(self):
    if len(self.memory.store) < 10:
        return  # Not enough experiences yet

    for state, action, reward in self.memory.retrieve():
        state_tensor = torch.tensor(state, dtype=torch.float32)
        predicted_rewards = self.model(state_tensor)

        target = predicted_rewards.clone()
        target[action] = reward

        loss = self.criterion(predicted_rewards, target.detach())

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

def set_goal(self, new_goal):
    """Set a new internal goal for strategic planning."""
    self.goal = new_goal
    print(f"New goal set: {self.goal}")

def adjust_learning(self):
    """Meta-learning: Adjust learning rate based on recent success."""
    if self.memory.store and np.mean([r[2] for r in self.memory.store[-10:]]) > 0.5:
        for param_group in self.optimizer.param_groups:
            param_group['lr'] *= 1.1  # Increase learning rate if performing well
    elif self.memory.store:
        for param_group in self.optimizer.param_groups:
            param_group['lr'] *= 0.9  # Decrease if struggling

Example environment interaction

if name == "main":
agent = AGIAgent(input_dim=10, hidden_dim=128, output_dim=4)
agent.set_goal("Maximize positive rewards while exploring efficiently.")

for episode in range(1000):  # Extended interaction loop for deeper learning
    state = np.random.rand(10)
    action = agent.choose_action(state)
    reward = np.random.rand() * (1 if action % 2 == 0 else -1)  # Structured reward
    agent.memory.remember(state, action, reward)
    agent.train()
    agent.adjust_learning()  # Optimize learning process dynamically

print("Training completed. The AGI model has learned from experience.")