import math import random import re from collections import deque import numpy as np
try: _ = AdvancedEvolvingOSSimulation except NameError: class EvolvingOSSimulation: def init(self, initial_state=None, learning_rate=0.01): self.system_state = initial_state if initial_state is not None else {} self.learning_rate = learning_rate self.action_history = deque(maxlen=100) # To store history for context
class AdvancedEvolvingOSSimulation(EvolvingOSSimulation):
def __init__(self, initial_state=None, learning_rate=0.1, decay_rate=0.95, context_window=3):
super().__init__(initial_state=initial_state, learning_rate=learning_rate)
self.system_state = initial_state if initial_state is not None else {
'resource_share_multimedia': 0.6,
'priority_level_multimedia': 7,
'latency_tolerance_multimedia': 0.1,
'resource_share_background': 0.2,
'priority_level_background': 3,
'latency_tolerance_background': 10.0,
'responsiveness_weight': 0.8,
'throughput_weight': 0.2,
}
self.decay_rate = decay_rate
self.context_window = context_window
self.action_value_estimates = {}
print("Placeholder Advanced simulation initialized.")
class HyperAdvancedEvolvingOSSimulation(AdvancedEvolvingOSSimulation): """ A further evolution, incorporating: - More complex, potentially non-linear state representation (e.g., features). - More sophisticated intent parsing (NLP/NLU integration). - Policy Gradient or Actor-Critic reinforcement learning. - A simulated environment with multiple, interacting processes and resources. - External factors simulation (network congestion, CPU temperature). - Uncertainty modeling (Bayesian methods). - Explainable AI elements (trace of decision process). """
def __init__(self, initial_state=None, learning_rate=0.001, decay_rate=0.98, context_window=5, feature_dim=10):
super().__init__(initial_state=initial_state, learning_rate=learning_rate, decay_rate=decay_rate, context_window=context_window)
self.feature_dim = feature_dim
# Initialize internal system state (parameters the OS can control/learn)
self.system_state = initial_state if initial_state is not None else {
'resource_share_multimedia': 0.6, # % of CPU/GPU/Memory allocated
'priority_level_multimedia': 7, # Priority queue level (1-10)
'latency_tolerance_multimedia': 0.1, # Acceptable latency (seconds)
'resource_share_background': 0.2,
'priority_level_background': 3,
'latency_tolerance_background': 10.0,
'resource_share_networking': 0.1,
'priority_level_networking': 5,
'responsiveness_weight': 0.8, # Internal weight for responsiveness in optimization
'throughput_weight': 0.2, # Internal weight for throughput
'energy_efficiency_weight': 0.1, # New: weight for energy
'disk_io_priority_factor': 0.5, # New: influence on disk I/O
'network_bandwidth_target': 1000, # New: MB/s target
}
self.system_features = self._state_to_features(self.system_state)
# Policy model (Actor): maps state features to probabilities of taking actions.
# Storing weights for each possible action, each weight vector corresponds to feature_dim
self.all_possible_actions = self._generate_all_possible_actions()
self.policy_weights = {str(action): np.random.uniform(-0.1, 0.1, self.feature_dim)
for action in self.all_possible_actions}
# Value model (Critic): maps state features to estimated value of the state.
self.value_weights = np.random.uniform(-0.1, 0.1, self.feature_dim)
# Simulated Environment: tracks dynamic resource usage, performance metrics
self.environment = {
'cpu_load': 0.5, # Current overall CPU load (0-1)
'memory_pressure': 0.3, # Current memory pressure (0-1)
'network_congestion': 0.2, # External network congestion (0-1)
'disk_io_queue': 0.1, # Disk I/O queue length (0-1)
'temperature': 50, # Simulated system temperature (C)
# Observed performance metrics (influenced by system_state and environment factors)
'observed_multimedia_latency': 0.15, # Actual latency for multimedia tasks
'observed_background_throughput': 800, # Actual throughput for background tasks (MB/s)
'observed_energy_consumption': 70, # Actual energy consumption (Watts)
}
print("Hyper Advanced simulation initialized.")
print("Initial system features (example):", self.system_features[:3])
# print("Initial policy weights (sample):", {k: v[:3] for k, v in list(self.policy_weights.items())[:3]})
# print("Initial value weights:", self.value_weights[:3])
def _state_to_features(self, state):
"""
Converts the internal system state dictionary and current environment
into a numerical feature vector. This is the OS's 'perception'.
"""
# Complex mapping: normalized values, interaction terms, environment context
features = [
state.get('resource_share_multimedia', 0),
state.get('priority_level_multimedia', 0) / 10.0,
state.get('latency_tolerance_multimedia', 0),
state.get('resource_share_background', 0),
state.get('priority_level_background', 0) / 10.0,
state.get('responsiveness_weight', 0),
state.get('throughput_weight', 0),
state.get('energy_efficiency_weight', 0),
state.get('disk_io_priority_factor', 0),
state.get('network_bandwidth_target', 0) / 2000.0, # Normalize large value
# Incorporate environment features directly into the state for decision making
self.environment.get('cpu_load', 0),
self.environment.get('memory_pressure', 0),
self.environment.get('network_congestion', 0),
self.environment.get('disk_io_queue', 0),
self.environment.get('temperature', 0) / 100.0, # Normalize temp
self.environment.get('observed_multimedia_latency', 0),
self.environment.get('observed_background_throughput', 0) / 1000.0,
self.environment.get('observed_energy_consumption', 0) / 100.0,
# Add interaction terms or non-linearities
(state.get('resource_share_multimedia', 0) * self.environment.get('cpu_load', 0)),
(state.get('priority_level_background', 0) / 10.0 * self.environment.get('disk_io_queue', 0))
]
# Pad or truncate features to match feature_dim
features = (features + [0.0] * self.feature_dim)[:self.feature_dim]
return np.array(features)
def _generate_all_possible_actions(self):
""" Generates a representative set of possible atomic actions.
Actions now modify the system_state directly.
"""
actions = []
# Define base increments/decrements for parameters
param_changes = {
'resource_share': 0.05,
'priority_level': 1,
'latency_tolerance': 0.01,
'weight': 0.05,
'disk_io_priority_factor': 0.1,
'network_bandwidth_target': 50
}
# Iterate over all system_state parameters to generate adjust actions
for param in self.system_state.keys():
base_name = ''
if 'resource_share' in param: base_name = 'resource_share'
elif 'priority_level' in param: base_name = 'priority_level'
elif 'latency_tolerance' in param: base_name = 'latency_tolerance'
elif 'weight' in param: base_name = 'weight'
elif 'disk_io_priority_factor' in param: base_name = 'disk_io_priority_factor'
elif 'network_bandwidth_target' in param: base_name = 'network_bandwidth_target'
if base_name:
amount = param_changes.get(base_name, 0)
if amount > 0:
actions.append({'type': 'adjust_param', 'param': param, 'direction': 'increase', 'amount': amount})
actions.append({'type': 'adjust_param', 'param': param, 'direction': 'decrease', 'amount': amount})
# Add a "no-op" action
actions.append({'type': 'no_op'})
return actions
def _parse_intent(self, intent_string):
"""
Sophisticated intent parsing using NLP/NLU concepts.
Extracts goals, metrics, task types, constraints, and sentiment.
"""
intent_string = intent_string.lower()
parsed_intent = {
'goals': [],
'target_metrics': [],
'task_types': [],
'constraints': {},
'sentiment': 0 # -1 (negative), 0 (neutral), 1 (positive)
}
# Goals (more nuanced keywords)
if re.search(r'\b(optimize|improve|boost|accelerate|speed up)\b', intent_string):
parsed_intent['goals'].append('optimize')
if re.search(r'\b(prioritize|focus on|dedicate resources)\b', intent_string):
parsed_intent['goals'].append('prioritize')
if re.search(r'\b(reduce|decrease|conserve|minimize)\b', intent_string):
parsed_intent['goals'].append('reduce')
if re.search(r'\b(maintain|keep stable|ensure consistency)\b', intent_string):
parsed_intent['goals'].append('maintain')
# Target metrics
if re.search(r'\b(responsiveness|latency|lag|delay)\b', intent_string):
parsed_intent['target_metrics'].append('responsiveness')
if re.search(r'\b(throughput|speed|bandwidth|data transfer)\b', intent_string):
parsed_intent['target_metrics'].append('throughput')
if re.search(r'\b(energy|power|battery life)\b', intent_string):
parsed_intent['target_metrics'].append('energy_efficiency')
if re.search(r'\b(cpu usage|processor load)\b', intent_string):
parsed_intent['target_metrics'].append('cpu_load')
if re.search(r'\b(memory usage|ram)\b', intent_string):
parsed_intent['target_metrics'].append('memory_pressure')
# Task types
if re.search(r'\b(multimedia|video|audio|streaming|gaming)\b', intent_string):
parsed_intent['task_types'].append('multimedia')
if re.search(r'\b(background|computation|batch processing|rendering|ai task)\b', intent_string):
parsed_intent['task_types'].append('background')
if re.search(r'\b(network|download|upload|web Browse)\b', intent_string):
parsed_intent['task_types'].append('networking')
if re.search(r'\b(disk|file operations|io)\b', intent_string):
parsed_intent['task_types'].append('disk_io')
# Constraints: numerical targets, probabilities, timeframes
# Example: "latency to 0.05s", "throughput by 90%", "80% probability"
num_val_match = re.search(r'(\d+\.?\d*)\s*(?:%|ms|s|mb/s|gb/s|watts?)?', intent_string)
if num_val_match:
value = float(num_val_match.group(1))
unit = num_val_match.group(2) if num_val_match.group(2) else ''
if '%' in unit:
parsed_intent['constraints']['percentage_target'] = value / 100.0
elif 'ms' in unit:
parsed_intent['constraints']['time_target_seconds'] = value / 1000.0
elif 's' in unit:
parsed_intent['constraints']['time_target_seconds'] = value
elif 'mb/s' in unit or 'gb/s' in unit:
parsed_intent['constraints']['throughput_target'] = value * (1000 if 'gb/s' in unit else 1)
elif 'watts' in unit:
parsed_intent['constraints']['energy_target_watts'] = value
prob_match = re.search(r'(\d+)% probability', intent_string)
if prob_match:
parsed_intent['constraints']['prob_target'] = int(prob_match.group(1)) / 100.0
time_match = re.search(r'within (\d+)\s?(minutes|seconds)', intent_string)
if time_match:
time_value = int(time_match.group(1))
time_unit = time_match.group(2)
parsed_intent['constraints']['time_target_seconds'] = time_value * (60 if 'minute' in time_unit else 1)
# Basic Sentiment Analysis (keywords only)
if re.search(r'\b(urgent|critical|immediately|now)\b', intent_string):
parsed_intent['sentiment'] = 1 # More positive/urgent
if re.search(r'\b(slow|bad|laggy|poor)\b', intent_string):
parsed_intent['sentiment'] = -1 # Negative
print("Parsed Hyper-Advanced intent:", parsed_intent)
return parsed_intent
def _policy_network_output_raw_scores(self, features):
"""
The 'Actor' part: Calculates raw scores (logits) for each possible action
given the current state features, using the policy weights.
"""
raw_scores = {}
for action_str, weights in self.policy_weights.items():
# Dot product of features and action-specific weights
raw_scores[action_str] = np.dot(features, weights)
return raw_scores
def _select_action_probabilistically(self, current_features, parsed_intent):
"""
Selects an action based on the policy network's output probabilities.
Uses softmax to convert raw scores to probabilities, then samples.
"""
raw_scores = self._policy_network_output_raw_scores(current_features)
# Adjust raw scores based on intent sentiment - a simple form of bias
if parsed_intent['sentiment'] > 0: # If urgent/positive sentiment
for action_str, score in raw_scores.items():
if 'increase' in action_str or 'prioritize' in action_str: # Favor actions that increase/prioritize
raw_scores[action_str] += 0.5 # Boost scores
elif parsed_intent['sentiment'] < 0: # If negative sentiment
for action_str, score in raw_scores.items():
if 'decrease' in action_str or 'reduce' in action_str: # Favor actions that decrease/reduce
raw_scores[action_str] += 0.5 # Boost scores
# Convert raw scores to probabilities using softmax
# Add a small epsilon to avoid math.exp(large_number) overflow if not using np.exp robustly
exp_scores = np.exp(list(raw_scores.values()) - np.max(list(raw_scores.values()))) # For numerical stability
probabilities = exp_scores / np.sum(exp_scores)
# Select an action based on these probabilities
selected_action_str = np.random.choice(list(raw_scores.keys()), p=probabilities)
selected_action = eval(selected_action_str) # Convert string representation back to dict
print(f"Action probabilities: {[f'{p:.2f}' for p in probabilities]}")
print(f"Selected action by policy: {selected_action}")
return selected_action, probabilities[list(raw_scores.keys()).index(selected_action_str)]
def _get_state_value(self, features):
"""
The 'Critic' part: Estimates the value of a given state (feature vector)
using the value weights.
"""
return np.dot(features, self.value_weights)
def _apply_action_to_state(self, current_state, action):
"""
Applies the selected action to the current system_state parameters.
Includes clipping to keep parameters within logical bounds.
"""
new_state = current_state.copy()
if action['type'] == 'adjust_param':
param = action['param']
amount = action['amount']
direction = action['direction']
if param in new_state:
change = amount if direction == 'increase' else -amount
new_state[param] += change
# Apply clipping to keep parameters within valid ranges
if 'resource_share' in param:
new_state[param] = max(0.0, min(1.0, new_state[param]))
elif 'priority_level' in param:
new_state[param] = max(1, min(10, new_state[param]))
elif 'latency_tolerance' in param:
new_state[param] = max(0.01, new_state[param]) # Latency tolerance must be positive
elif 'weight' in param:
new_state[param] = max(0.0, min(1.0, new_state[param])) # Weights between 0 and 1
elif 'disk_io_priority_factor' in param:
new_state[param] = max(0.0, min(1.0, new_state[param]))
elif 'network_bandwidth_target' in param:
new_state[param] = max(100, new_state[param]) # Min bandwidth
return new_state
def _update_environment(self, current_state_after_action):
"""
Simulates the dynamic changes in the OS environment.
This is where the complex, emergent behavior happens.
The current_state_after_action (our learned policy parameters)
directly influences the environment's observed metrics.
External factors (noise, trends) also play a role.
"""
# Simulate external factors fluctuating (noise, trends)
self.environment['cpu_load'] = max(0.0, min(1.0, self.environment['cpu_load'] + random.uniform(-0.05, 0.05)))
self.environment['memory_pressure'] = max(0.0, min(1.0, self.environment['memory_pressure'] + random.uniform(-0.03, 0.03)))
self.environment['network_congestion'] = max(0.0, min(1.0, self.environment['network_congestion'] + random.uniform(-0.04, 0.04)))
self.environment['disk_io_queue'] = max(0.0, min(1.0, self.environment['disk_io_queue'] + random.uniform(-0.02, 0.02)))
self.environment['temperature'] = max(30, min(90, self.environment['temperature'] + random.uniform(-2, 2))) # Simulate temperature
# Simulate observed performance metrics based on current system_state (policy)
# and current environment factors. This is the 'true' system dynamics.
# Multimedia Latency (lower is better):
# Influenced negatively by multimedia resource share (less latency with more share),
# positively by multimedia priority, and positively by network congestion/cpu load.
self.environment['observed_multimedia_latency'] = max(0.01,
(1.0 - current_state_after_action['resource_share_multimedia']) * 0.2 +
(10 - current_state_after_action['priority_level_multimedia']) * 0.01 +
self.environment['network_congestion'] * 0.15 +
self.environment['cpu_load'] * 0.1 +
random.gauss(0, 0.02) # Noise
)
# Background Throughput (higher is better):
# Influenced positively by background resource share/priority,
# negatively by disk I/O queue / memory pressure.
self.environment['observed_background_throughput'] = max(10,
current_state_after_action['resource_share_background'] * 1000 +
current_state_after_action['priority_level_background'] * 50 -
self.environment['disk_io_queue'] * 200 -
self.environment['memory_pressure'] * 150 +
random.gauss(0, 50) # Noise
)
# Energy Consumption (lower is better):
# Influenced by total resource shares, temperature, and current performance weights.
self.environment['observed_energy_consumption'] = max(10,
(current_state_after_action['resource_share_multimedia'] +
current_state_after_action['resource_share_background'] +
current_state_after_action['resource_share_networking']) * 30 +
self.environment['temperature'] * 0.5 +
current_state_after_action['responsiveness_weight'] * 10 + # Higher weights for performance might mean more energy
current_state_after_action['throughput_weight'] * 5 +
random.gauss(0, 5) # Noise
)
print("Updated Environment State:", {k: f"{v:.2f}" for k, v in self.environment.items()})
def _calculate_reward(self, parsed_intent, current_environment, state_before_action, state_after_action):
"""
Calculates a complex reward signal based on how well the system's
performance (from the environment) meets the user's intent and internal goals.
This is the OS's 'evaluation function'.
"""
reward = 0.0
# Base reward for meeting general system health goals (e.g., balanced load, low temperature)
reward += (1.0 - self.environment['cpu_load']) * 0.1
reward += (1.0 - self.environment['memory_pressure']) * 0.1
reward += (1.0 - (self.environment['temperature'] - 30) / 60) * 0.1 # Normalize 30-90C to 0-1
# Reward based on intent goals and target metrics
if 'optimize' in parsed_intent['goals'] or 'prioritize' in parsed_intent['goals']:
if 'responsiveness' in parsed_intent['target_metrics']:
# Penalize high latency, reward low latency
latency_factor = self.environment['observed_multimedia_latency'] / current_state_after_action['latency_tolerance_multimedia']
reward -= max(0, latency_factor - 1.0) * 2.0 # Strong penalty if exceeds tolerance
reward += (1.0 - latency_factor) * current_state_after_action['responsiveness_weight'] * 1.0 # Reward for being below tolerance
# Check specific latency targets
if 'time_target_seconds' in parsed_intent['constraints']:
target_lat = parsed_intent['constraints']['time_target_seconds']
if self.environment['observed_multimedia_latency'] <= target_lat:
reward += 1.0 # Significant bonus for meeting explicit target
else:
reward -= (self.environment['observed_multimedia_latency'] - target_lat) * 2.0 # Penalty for missing target
if 'throughput' in parsed_intent['target_metrics']:
# Reward high throughput
current_throughput = self.environment['observed_background_throughput']
reward += (current_throughput / 1000.0) * current_state_after_action['throughput_weight'] * 1.0 # Normalized throughput
# Check specific throughput targets
if 'throughput_target' in parsed_intent['constraints']:
target_thp = parsed_intent['constraints']['throughput_target']
if current_throughput >= target_thp:
reward += 1.0
else:
reward -= (target_thp - current_throughput) / 500.0
if 'energy_efficiency' in parsed_intent['target_metrics']:
# Reward low energy consumption
reward -= (self.environment['observed_energy_consumption'] / 100.0) * current_state_after_action['energy_efficiency_weight'] * 0.5 # Penalty for higher energy
if 'energy_target_watts' in parsed_intent['constraints']:
target_energy = parsed_intent['constraints']['energy_target_watts']
if self.environment['observed_energy_consumption'] <= target_energy:
reward += 0.5
else:
reward -= (self.environment['observed_energy_consumption'] - target_energy) / 50.0
# Penalty for violating probability targets (conceptually, over many episodes)
# This is more for the overall learning trend than a single instance.
# If the actual performance probability (observed success rate over time) is below the target,
# it would feed into a meta-learning reward. For a single step, it's indirect.
# Penalty for large, unnecessary state changes (discourage volatility if not explicitly requested)
# Calculate diff between state_before_action and state_after_action
change_penalty = 0
for param, val_after in state_after_action.items():
if param in state_before_action:
change_penalty += abs(val_after - state_before_action[param])
reward -= change_penalty * 0.1 # Small penalty for change magnitude
# Boost reward if intent sentiment is positive and goals are met
if parsed_intent['sentiment'] > 0 and reward > 0:
reward *= 1.2
# Penalize more if sentiment is negative and goals are missed
elif parsed_intent['sentiment'] < 0 and reward <= 0:
reward *= 1.5
print(f"Calculated reward: {reward:.4f}")
return reward
def _learn_from_outcome(self, action, reward, prob_selected_action, state_before_features, state_after_features):
"""
Implements Actor-Critic learning:
1. Critic (Value Network) Update: Learns to predict the value of a state.
2. Actor (Policy Network) Update: Learns to take actions that lead to higher rewards.
"""
# Critic Update (Value Network)
current_state_value = self._get_state_value(state_before_features)
next_state_value = self._get_state_value(state_after_features) # Value of the state AFTER action and environment update
# Temporal Difference (TD) error: R + gamma * V(s') - V(s)
td_target = reward + self.decay_rate * next_state_value
td_error = td_target - current_state_value
# Update Critic (Value Weights)
# Apply gradient descent to minimize TD error
self.value_weights += self.learning_rate * td_error * state_before_features
# Actor Update (Policy Network)
# Advantage is typically TD_error for simple Actor-Critic
advantage = td_error
# Policy Gradient Update for the selected action
# The goal is to increase log(probability of action) for good actions (positive advantage)
# and decrease for bad actions (negative advantage).
selected_action_str = str(action)
# Calculate gradient of log probability w.r.t. policy weights (simplified)
# For a linear policy with softmax output, the gradient of log_prob(a|s) w.r.t. W_a is (features - sum(prob_j * features_j))
# Here, a simpler form where the gradient is proportional to the state features
# and scaled by (1 - prob) or (prob - 1) - this needs careful derivation for true softmax.
# For simplicity, we approximate: if an action was good (positive advantage), reinforce its weights.
# if an action was bad (negative advantage), weaken its weights.
# This is a very simplified policy gradient update for a linear model
self.policy_weights[selected_action_str] += self.learning_rate * advantage * state_before_features
# Optional: Add entropy bonus to encourage exploration (discourage deterministic policies)
# entropy_bonus = -np.sum(probabilities * np.log(probabilities + 1e-8)) # Add small epsilon for log(0)
# for action_str in self.policy_weights:
# self.policy_weights[action_str] += self.learning_rate * 0.01 * entropy_bonus * state_before_features # Small constant for entropy
print(f"Learning: TD Error: {td_error:.4f}. Updated Actor and Critic weights.")
def process_and_adapt(self, intent_string):
"""
Processes a user intent using Hyper-Advanced methods.
This is the main loop for an interaction.
"""
print("\n--- Processing New Hyper-Advanced Intent ---")
print("Intent:", intent_string)
# 1. Capture current state features (before any action)
state_before_features = self.system_features.copy()
state_before_params = self.system_state.copy() # Capture current policy parameters
# 2. Parse the user's intent
parsed_intent = self._parse_intent(intent_string)
if not parsed_intent or (not parsed_intent['goals'] and not parsed_intent['task_types']):
print("Could not parse meaningful intent.")
return
# 3. Policy (Actor) selects an action based on current features and intent
# (Candidate actions are predefined; the policy learns to choose among them)
selected_action, prob_selected_action = self._select_action_probabilistically(state_before_features, parsed_intent)
# 4. Apply the selected action to the OS's internal state (policy parameters)
self.system_state = self._apply_action_to_state(state_before_params, selected_action)
print("System state (policy parameters) after action:", {k:f"{v:.3f}" for k,v in self.system_state.items()})
# 5. Update the simulated environment based on the new system state
# This simulates how the OS's internal changes affect real-world performance metrics
self._update_environment(self.system_state)
# 6. Re-capture features of the new state for the Critic
state_after_features = self._state_to_features(self.system_state)
# 7. Calculate the reward based on the outcome in the environment
reward = self._calculate_reward(parsed_intent, self.environment, state_before_params, self.system_state)
# 8. Learn from the outcome (Actor-Critic update)
self._learn_from_outcome(selected_action, reward, prob_selected_action, state_before_features, state_after_features)
# 9. Store history for context and analysis
self.action_history.append({
'intent': intent_string,
'parsed_intent': parsed_intent,
'selected_action': selected_action,
'prob_selected_action': prob_selected_action,
'reward': reward,
'state_before_params': state_before_params,
'state_after_params': self.system_state.copy(),
'environment_after': self.environment.copy()
})
print("--- Hyper-Advanced Intent Processed ---")
print("\n\n--- Starting Hyper Advanced Simulation ---")
# Initialize the hyper advanced simulated system
hyper_adv_sim_os = HyperAdvancedEvolvingOSSimulation(feature_dim=20, learning_rate=0.005) # Increased feature_dim for more complexity
# Process intents, observing state changes and learning
# Run multiple times to observe learning and adaptation
for i in range(1):
print(f"\n===== Episode {i+1} =====")
hyper_adv_sim_os.process_and_adapt("Optimize system responsiveness for multimedia, with an 80% chance within 0.08 seconds. It's urgent!")
hyper_adv_sim_os.process_and_adapt("Prioritize background computation, ensure 900 MB/s throughput.")
hyper_adv_sim_os.process_and_adapt("Reduce energy consumption to 60 watts. Conserve battery life.")
hyper_adv_sim_os.process_and_adapt("I'm experiencing terrible lag with streaming video. Fix it now!")
hyper_adv_sim_os.process_and_adapt("Boost network bandwidth for downloads, target 1200 MB/s.")
hyper_adv_sim_os.process_and_adapt("Maintain stable CPU usage and minimize memory pressure.")
hyper_adv_sim_os.process_and_adapt("Optimize responsiveness for multimedia tasks, with an 80% chance within 0.08 seconds. It's urgent!") # Repeat to see learning effect
hyper_adv_sim_os.process_and_adapt("Prioritize background computation, ensure 900 MB/s throughput.") # Repeat
# Inspect final policy parameters and environment state after extensive learning
print("\n--- Final System State (Policy Parameters) after Hyper Advanced Simulation ---")
for k, v in hyper_adv_sim_os.system_state.items():
print(f" {k}: {v:.4f}")
print("\n--- Final Environment State ---")
for k, v in hyper_adv_sim_os.environment.items():
print(f" {k}: {v:.4f}")
print("\n--- Sample of Action History (last 5 entries) ---")
for entry in list(hyper_adv_sim_os.action_history)[-5:]:
print(f" Intent: {entry['intent']}")
print(f" Action: {entry['selected_action']}")
print(f" Reward: {entry['reward']:.4f}")
print(f" Observed Latency: {entry['environment_after']['observed_multimedia_latency']:.4f}")
print(f" Observed Throughput: {entry['environment_after']['observed_background_throughput']:.4f}")
print(f" Observed Energy: {entry['environment_after']['observed_energy_consumption']:.4f}")
print("-" * 20)
# Further analysis could involve plotting trends of parameters, rewards,
# and environment metrics over many more interactions to truly see learning convergence.