import numpy as np
import pandas as pd
import gym
from gym import spaces
import requests
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.callbacks import EvalCallback
import random
import json
import time
class PlanetaryResourceEnv(gym.Env):
"""Custom RL environment simulating planetary resource allocation."""
def __init__(self, num_regions=10, resources=['water', 'energy', 'minerals']):
super().__init__()
self.num_regions = num_regions
self.resources = resources
self.state = self._init_state() # {region: {resource: amount}}
self.action_space = spaces.Box(low=0, high=1, shape=(num_regions * len(resources),), dtype=np.float32) # Allocation ratios
self.observation_space = spaces.Box(low=0, high=1e6, shape=(num_regions * len(resources),), dtype=np.float32)
self.steps = 0
self.max_steps = 1000
def _init_state(self):
return {f"region_{i}": {r: random.uniform(1000, 10000) for r in self.resources} for i in range(self.num_regions)}
def _get_obs(self):
return np.array([self.state[r][res] for r in self.state for res in self.resources])
def step(self, action):
# Decode action into allocations
allocations = action.reshape((self.num_regions, len(self.resources)))
total_available = {r: sum(self.state[reg][r] for reg in self.state) for r in self.resources}
rewards = 0
for i, reg in enumerate(self.state):
for j, res in enumerate(self.resources):
allocated = allocations[i][j] * total_available[res]
self.state[reg][res] = max(0, self.state[reg][res] + allocated - random.uniform(500, 1500)) # Simulate usage/demand
# Reward: Balance (minimize variance) + sustainability (penalize overuse)
variances = [np.var([self.state[r][res] for r in self.state]) for res in self.resources]
reward_balance = -sum(variances) # Negative variance for balance
reward_sustain = -sum(1 for r in self.resources if total_available[r] < 5000) * 100 # Penalty for shortages
rewards = reward_balance + reward_sustain
self.steps += 1
done = self.steps >= self.max_steps
return self._get_obs(), rewards, done, {}
def reset(self):
self.state = self._init_state()
self.steps = 0
return self._get_obs()
class ResourceOptimizer:
def __init__(self, num_regions=10, model_path="ppo_gaia.zip"):
self.num_regions = num_regions
self.env = make_vec_env(lambda: PlanetaryResourceEnv(num_regions), n_envs=4) # Vectorized for speed
self.model = PPO("MlpPolicy", self.env, verbose=1, learning_rate=0.0003, n_steps=2048)
self.model_path = model_path
self.fairness_threshold = 0.3 # Gini coefficient limit for equity
def train(self, total_timesteps=10000, real_data_augmentation=True):
"""Train RL model with optional real data."""
if real_data_augmentation:
self._augment_with_real_data()
eval_callback = EvalCallback(self.env, best_model_save_path="./logs/", log_path="./logs/", eval_freq=1000)
self.model.learn(total_timesteps=total_timesteps, callback=eval_callback)
self.model.save(self.model_path)
def _augment_with_real_data(self):
"""Augment training with real-world data (e.g., weather for water needs)."""
try:
# Example: Fetch weather data (replace with API key)
response = requests.get("https://api.openweathermap.org/data/2.5/weather?q=London&appid=your_api_key")
data = response.json()
temp = data['main']['temp'] # Simulate demand based on temp
# Adjust env dynamics (simplified)
self.env.env_fns[0]().state['region_0']['water'] *= (1 + (temp - 273) / 100) # Higher temp = more demand
except:
print("Real data fetch failed; using synthetic.")
def optimize_allocation(self, regions_data):
"""Predict allocations using trained model."""
obs = np.array([d for sublist in regions_data for d in sublist]) # Flatten
action, _ = self.model.predict(obs, deterministic=True)
allocations = action.reshape((self.num_regions, len(PlanetaryResourceEnv().resources)))
# Apply fairness: Adjust for Gini
gini = self._calculate_gini(allocations.flatten())
if gini > self.fairness_threshold:
allocations = self._redistribute_for_fairness(allocations)
return allocations.tolist()
def _calculate_gini(self, array):
"""Calculate Gini coefficient for fairness."""
array = np.sort(array)
n = len(array)
cumsum = np.cumsum(array)
return (n + 1 - 2 * np.sum(cumsum) / cumsum[-1]) / n
def _redistribute_for_fairness(self, allocations):
"""Self-correct for equity."""
flat = allocations.flatten()
mean = np.mean(flat)
return np.clip(flat + (mean - flat) * 0.1, 0, None).reshape(allocations.shape) # Dampen extremes
def simulate_homeostasis(self, steps=100):
"""Simulate self-correcting oscillations."""
obs = self.env.reset()
history = []
for _ in range(steps):
action, _ = self.model.predict(obs)
obs, reward, done, _ = self.env.step(action)
history.append({"step": _, "reward": reward, "obs": obs.tolist()})
if done:
break
return history
def integrate_with_quantum_iot(self, quantum_ledger, iot_simulator):
"""Full integration: Sync with quantum ledger and IoT for planetary optimization."""
iot_data = iot_simulator.simulate_tracking()
regions_data = [[d['water_level'], d['energy_usage'], random.uniform(0, 1000)] for d in iot_data.values()]
allocations = self.optimize_allocation(regions_data)
# Simulate planetary data
planetary_data = {f"region_{i}": {"water": allocations[i][0], "energy": allocations[i][1], "minerals": allocations[i][2]} for i in range(len(allocations))}
synced_ledgers, consensus = quantum_ledger.multi_node_sync(planetary_data)
return synced_ledgers, consensus, allocations
# Example Usage (Runnable Standalone)
if __name__ == "__main__":
from quantum_ledger import QuantumLedger # Import from same folder
from iot_simulator import IoTSimulator
optimizer = ResourceOptimizer()
ledger = QuantumLedger()
simulator = IoTSimulator()
# Train model (short for demo; run longer for better results)
optimizer.train(total_timesteps=5000)
# Run integration
synced, consensus, allocs = optimizer.integrate_with_quantum_iot(ledger, simulator)
print("Optimized Allocations:", json.dumps(allocs, indent=2))
print("Synced Ledgers:", json.dumps(synced, indent=2))
print("Consensus:", consensus)
# Simulate homeostasis
history = optimizer.simulate_homeostasis(50)
print("Homeostasis History (Sample):", history[:5])
