entropy_collapsing_process.py

class EntropyCollapsingProcess:
    def __init__(self, initial_condition):
        self.condition = initial_condition
        self.history = []
    
    def search(self, condition) -> tuple[float, Distribution]:
        """
        Given current condition, returns:
        - entropy: effective bits of uncertainty in the distribution
        - action_dist: probability distribution over possible actions
        """
        raise NotImplementedError
    
    def sample_action(self, action_dist) -> Action:
        """Sample from the action distribution"""
        return action_dist.sample()
    
    def strengthen_condition(self, condition, action) -> Condition:
        """Update condition to include sampled action"""
        return condition.union(action)
    
    def step(self):
        # Search surfaces a probability distribution with some entropy
        entropy, action_dist = self.search(self.condition)
        
        # Sample from the distribution
        action = self.sample_action(action_dist)
        
        # Update condition with sampled action
        self.condition = self.strengthen_condition(self.condition, action)
        
        # Track history
        self.history.append({
            'entropy': entropy,
            'action': action
        })
        
        return entropy, action

    def run_until_confident(self, entropy_threshold=0.1, max_steps=1000):
        for _ in range(max_steps):
            entropy, action = self.step()
            if entropy < entropy_threshold:  # Distribution is very peaked
                break