---
title: Implementing Active Inference Agents
type: guide
status: stable
created: 2024-02-07
tags:
  - active_inference
  - implementation
  - guide
semantic_relations:
  - type: implements
    links: [[knowledge_base/cognitive/active_inference]]
  - type: relates
    links:
      - [[knowledge_base/mathematics/free_energy_theory]]
      - [[knowledge_base/agents/GenericPOMDP/README]]
---

# Implementing Active Inference Agents

## Overview

This guide provides a comprehensive approach to implementing active inference agents, from basic principles to advanced features. We'll cover the theoretical foundations, mathematical implementations, and practical considerations.

## Core Components

### 1. Generative Model
```python
class GenerativeModel:
    """
    Generative model for active inference agent.
    
    Implements:
    - State transition model P(s_t | s_t-1, a_t)
    - Observation model P(o_t | s_t)
    - Prior preferences P(s_t)
    """
    
    def __init__(self, config):
        self.state_dim = config.state_dim
        self.obs_dim = config.obs_dim
        self.action_dim = config.action_dim
        
        # Initialize model parameters
        self.transition_matrix = initialize_transitions()
        self.observation_matrix = initialize_observations()
        self.preferences = initialize_preferences()
        
    def state_transition(self, state, action):
        """Compute state transition probability."""
        return compute_transition_prob(
            state, action, self.transition_matrix
        )
        
    def observation_likelihood(self, state):
        """Compute observation likelihood."""
        return compute_observation_prob(
            state, self.observation_matrix
        )
        
    def prior_preference(self, state):
        """Compute prior preference."""
        return compute_preference(state, self.preferences)
```

### 2. Variational Inference
```python
class VariationalInference:
    """
    Implements variational inference for belief updating.
    """
    
    def __init__(self, model):
        self.model = model
        self.learning_rate = 0.1
        
    def update_beliefs(self, beliefs, observation):
        """Update beliefs using variational inference."""
        # Compute free energy gradients
        gradients = compute_free_energy_gradients(
            beliefs, observation, self.model
        )
        
        # Update beliefs using gradient descent
        updated_beliefs = beliefs - self.learning_rate * gradients
        
        # Normalize beliefs
        return normalize_distribution(updated_beliefs)
```

### 3. Policy Selection
```python
class PolicySelection:
    """
    Policy selection using expected free energy.
    """
    
    def __init__(self, model):
        self.model = model
        self.temperature = 1.0
        
    def evaluate_policies(self, beliefs, policies):
        """Evaluate policies using expected free energy."""
        G = np.zeros(len(policies))
        
        for i, policy in enumerate(policies):
            # Compute expected free energy components
            pragmatic_value = compute_pragmatic_value(
                beliefs, policy, self.model
            )
            epistemic_value = compute_epistemic_value(
                beliefs, policy, self.model
            )
            
            G[i] = pragmatic_value + epistemic_value
            
        return G
        
    def select_action(self, beliefs):
        """Select action using softmax policy selection."""
        policies = generate_policies()
        G = self.evaluate_policies(beliefs, policies)
        
        # Softmax policy selection
        probabilities = softmax(G / self.temperature)
        return sample_action(probabilities, policies)
```

## Implementation Steps

### 1. Basic Setup
```python
def setup_active_inference_agent(config):
    """Setup basic active inference agent."""
    # Create generative model
    model = GenerativeModel(config)
    
    # Setup inference
    inference = VariationalInference(model)
    
    # Setup policy selection
    policy = PolicySelection(model)
    
    return ActiveInferenceAgent(model, inference, policy)
```

### 2. Main Agent Class
```python
class ActiveInferenceAgent:
    """
    Complete active inference agent implementation.
    """
    
    def __init__(self, model, inference, policy):
        self.model = model
        self.inference = inference
        self.policy = policy
        
        # Initialize beliefs
        self.beliefs = initialize_beliefs(model.state_dim)
        
    def step(self, observation):
        """Single step of perception-action cycle."""
        # 1. Update beliefs
        self.beliefs = self.inference.update_beliefs(
            self.beliefs, observation
        )
        
        # 2. Select action
        action = self.policy.select_action(self.beliefs)
        
        # 3. Execute action
        return action
```

## Advanced Features

### 1. Hierarchical Processing
```python
class HierarchicalAgent:
    """
    Hierarchical active inference implementation.
    """
    
    def __init__(self, level_configs):
        self.levels = [
            ActiveInferenceAgent(config)
            for config in level_configs
        ]
        
    def update(self, observation):
        """Update all levels."""
        for level in self.levels:
            prediction = level.step(observation)
            observation = prediction  # Pass prediction as observation
```

### 2. Memory Integration
```python
class MemoryAugmentedAgent:
    """
    Agent with memory integration.
    """
    
    def __init__(self, config):
        super().__init__(config)
        self.memory = EpisodicMemory(config.memory_size)
        
    def step(self, observation):
        # Integrate memory into belief updating
        memory_state = self.memory.retrieve(self.beliefs)
        augmented_beliefs = integrate_memory(
            self.beliefs, memory_state
        )
        
        # Standard active inference step
        action = super().step(observation)
        
        # Update memory
        self.memory.store(self.beliefs, action, observation)
        return action
```

## Configuration Examples

### Basic Configuration
```yaml
agent_config:
  state_dim: 10
  obs_dim: 5
  action_dim: 3
  
  learning:
    learning_rate: 0.1
    temperature: 1.0
    
  model:
    hidden_dims: [64, 32]
    activation: "relu"
```

### Hierarchical Configuration
```yaml
hierarchical_config:
  levels:
    - state_dim: 20
      temporal_scale: 1
    - state_dim: 10
      temporal_scale: 5
    - state_dim: 5
      temporal_scale: 10
```

## Testing and Validation

### 1. Unit Tests
```python
def test_belief_updating():
    """Test belief updating mechanism."""
    agent = setup_test_agent()
    initial_beliefs = agent.beliefs.copy()
    
    observation = generate_test_observation()
    agent.step(observation)
    
    assert np.all(agent.beliefs != initial_beliefs)
    assert is_normalized(agent.beliefs)
```

### 2. Integration Tests
```python
def test_complete_cycle():
    """Test complete perception-action cycle."""
    agent = setup_test_agent()
    environment = setup_test_environment()
    
    observation = environment.reset()
    for _ in range(100):
        action = agent.step(observation)
        observation, reward, done, _ = environment.step(action)
        
        assert is_valid_action(action)
        if done:
            break
```

## Performance Optimization

### 1. Efficient Computation
```python
@numba.jit(nopython=True)
def compute_free_energy_gradients(beliefs, observation, model):
    """Optimized gradient computation."""
    # Efficient implementation
    pass
```

### 2. Parallel Processing
```python
class ParallelPolicyEvaluation:
    """Parallel policy evaluation."""
    
    def evaluate_policies(self, beliefs, policies):
        with concurrent.futures.ProcessPoolExecutor() as executor:
            G = list(executor.map(
                self._evaluate_single_policy,
                [(beliefs, p) for p in policies]
            ))
        return np.array(G)
```

## Common Issues and Solutions

### 1. Numerical Stability
```python
def stable_softmax(x):
    """Numerically stable softmax implementation."""
    x = x - np.max(x)  # Subtract maximum for stability
    exp_x = np.exp(x)
    return exp_x / np.sum(exp_x)
```

### 2. Belief Normalization
```python
def normalize_beliefs(beliefs, epsilon=1e-10):
    """Safe belief normalization."""
    beliefs = np.clip(beliefs, epsilon, None)
    return beliefs / np.sum(beliefs)
```

## Usage Example

```python
# Setup agent
config = load_config("agent_config.yaml")
agent = setup_active_inference_agent(config)

# Run simulation
environment = setup_environment()
observation = environment.reset()

for step in range(max_steps):
    # Agent step
    action = agent.step(observation)
    
    # Environment step
    observation, reward, done, info = environment.step(action)
    
    # Logging and visualization
    log_step(step, agent, observation, reward)
    visualize_state(agent, environment)
    
    if done:
        break
```

## References

- [[knowledge_base/cognitive/active_inference|Active Inference Theory]]
- [[knowledge_base/mathematics/free_energy_theory|Free Energy Theory]]
- [[knowledge_base/mathematics/variational_methods|Variational Methods]]
- [[examples/active_inference_basic|Basic Example]]