Learning Agent Architecture

Core Learning Loop:
Experience → Learning → Knowledge Update → Improved Performance

Performance Element

Executes actions based on current knowledge and policies

Learning Element

Analyzes feedback and updates knowledge base

Critic

Evaluates performance and provides feedback signals

Problem Generator

Suggests exploratory actions for learning opportunities

Knowledge Base

Stores learned patterns, rules, and experiences

Learning Agent Implementation

class LearningAgent:
    def __init__(self, learning_rate=0.1, exploration_rate=0.1):
        self.knowledge_base = {}
        self.experience_buffer = []
        self.learning_rate = learning_rate
        self.exploration_rate = exploration_rate
        self.performance_history = []
        
    def act(self, state):
        """Choose action based on current knowledge and exploration"""
        if random.random() < self.exploration_rate:
            # Exploration: try new actions
            action = self.explore_action(state)
        else:
            # Exploitation: use best known action
            action = self.best_action(state)
        
        return action
    
    def learn_from_experience(self, state, action, reward, next_state):
        """Update knowledge based on experience"""
        experience = {
            'state': state,
            'action': action,
            'reward': reward,
            'next_state': next_state,
            'timestamp': time.time()
        }
        
        self.experience_buffer.append(experience)
        
        # Update knowledge using various learning methods
        self.update_q_values(state, action, reward, next_state)
        self.update_patterns(experience)
        self.evaluate_performance()
        
        # Periodic knowledge consolidation
        if len(self.experience_buffer) % 100 == 0:
            self.consolidate_knowledge()
    
    def update_q_values(self, state, action, reward, next_state):
        """Q-learning update rule"""
        state_key = self.state_to_key(state)
        
        if state_key not in self.knowledge_base:
            self.knowledge_base[state_key] = {}
        
        current_q = self.knowledge_base[state_key].get(action, 0)
        max_next_q = max(
            self.knowledge_base.get(
                self.state_to_key(next_state), {}
            ).values(), 
            default=0
        )
        
        # Q-learning formula
        new_q = current_q + self.learning_rate * (
            reward + 0.9 * max_next_q - current_q
        )
        
        self.knowledge_base[state_key][action] = new_q
    
    def update_patterns(self, experience):
        """Learn patterns from experience"""
        # Pattern recognition and rule extraction
        patterns = self.extract_patterns(experience)
        for pattern in patterns:
            self.add_to_knowledge_base(pattern)
    
    def consolidate_knowledge(self):
        """Consolidate and optimize learned knowledge"""
        # Remove outdated experiences
        cutoff_time = time.time() - 3600  # 1 hour
        self.experience_buffer = [
            exp for exp in self.experience_buffer 
            if exp['timestamp'] > cutoff_time
        ]
        
        # Optimize knowledge representation
        self.optimize_knowledge_base()
    
    def adapt_learning_rate(self):
        """Dynamically adjust learning parameters"""
        recent_performance = self.performance_history[-10:]
        if len(recent_performance) >= 10:
            if self.is_improving(recent_performance):
                self.exploration_rate *= 0.95  # Reduce exploration
            else:
                self.exploration_rate *= 1.05  # Increase exploration
                
        # Decay learning rate over time
        self.learning_rate *= 0.999

# Example: Adaptive web scraping agent
class AdaptiveScrapingAgent(LearningAgent):
    def __init__(self):
        super().__init__()
        self.success_patterns = {}
        self.failure_patterns = {}
    
    def scrape_page(self, url):
        state = self.analyze_page_structure(url)
        action = self.act(state)
        
        try:
            result = self.execute_scraping_action(action, url)
            reward = self.calculate_reward(result)
            self.learn_from_experience(state, action, reward, None)
            return result
        except Exception as e:
            self.learn_from_experience(state, action, -1, None)
            return None

Implementation Challenges and Solutions

Catastrophic Forgetting

Agent forgets previously learned knowledge when learning new tasks or adapting to changes.

Solution: Implement experience replay, elastic weight consolidation, and progressive neural networks.

Exploration vs Exploitation

Balancing between trying new actions (exploration) and using known good actions (exploitation).

Solution: Use epsilon-greedy, UCB, or Thompson sampling strategies with adaptive parameters.

Sample Efficiency

Learning requires many examples and interactions, which can be expensive or time-consuming.

Solution: Use transfer learning, meta-learning, and few-shot learning techniques.

Safety and Robustness

Ensuring agent doesn't learn harmful behaviors or make dangerous decisions during exploration.

Solution: Implement safe exploration, constrained optimization, and human oversight mechanisms.

Learning Agents - The Most Advanced Type

Adaptive Intelligence

Learning Agent Architecture

Types of Learning in AI Agents

Implementation Challenges and Solutions