Dynamic State-Action Entropy

The problem

It is difficult to know which move provides the most useful information when facing a complex situation.

What it does

It looks at different possible actions and calculates which one will most effectively clear up uncertainty about the current situation.

Why it matters

It allows for making decisions based on what will actually teach you the most about your next steps.

Validation

It was run in the sandbox and it failed. run produced no meaningful output (empty or near-empty).

$ python3 dsa_module.py
Best action from state A: 1

No screenshot — there is nothing working to show. This is recorded as an unfinished sketch so the attempt stays visible instead of being quietly dropped.

The code

All of it — 42 lines, one file, standard library only.

import math

def entropy(probabilities):
    """Calculate Shannon entropy for a probability distribution."""
    return -sum(p * math.log2(p) for p in probabilities if p > 0)

def dynamic_state_action_entropy(transition_model, current_state):
    """Calculate entropy for each action and return the best one."""
    best_action = None
    min_entropy = float('inf')
    
    # Find all available actions for the current state
    actions = set((state, action) for (state, action) in transition_model.keys() if state == current_state)
    
    for state_action in actions:
        action = state_action[1]
        next_state_probs = list(transition_model[state_action].values())
        
        # Handle zero probabilities and validate distribution
        if len(next_state_probs) == 0 or abs(sum(next_state_probs) - 1) > 1e-6:
            continue  # Skip invalid distributions
        
        current_entropy = entropy(next_state_probs)
        if current_entropy < min_entropy:
            min_entropy = current_entropy
            best_action = action
    
    return best_action

# Example usage
if __name__ == "__main__":
    # Sample transition probability model
    transition_model = {
        ('A', 0): {'A': 0.7, 'B': 0.3},
        ('A', 1): {'B': 0.9, 'A': 0.1},
        ('B', 0): {'A': 0.4, 'B': 0.6},
        ('B', 1): {'A': 0.5, 'B': 0.5},
    }
    
    current_state = 'A'
    best_action = dynamic_state_action_entropy(transition_model, current_state)
    print(f"Best action from state {current_state}: {best_action}")