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362 строки
9.2 KiB
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362 строки
9.2 KiB
Markdown
---
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title: Implementing Active Inference Agents
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type: guide
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status: stable
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created: 2024-02-07
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tags:
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- active_inference
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- implementation
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- guide
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semantic_relations:
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- type: implements
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links: [[knowledge_base/cognitive/active_inference]]
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- type: relates
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links:
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- [[knowledge_base/mathematics/free_energy_theory]]
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- [[knowledge_base/agents/GenericPOMDP/README]]
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---
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# Implementing Active Inference Agents
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## Overview
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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.
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## Core Components
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### 1. Generative Model
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```python
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class GenerativeModel:
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"""
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Generative model for active inference agent.
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Implements:
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- State transition model P(s_t | s_t-1, a_t)
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- Observation model P(o_t | s_t)
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- Prior preferences P(s_t)
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"""
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def __init__(self, config):
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self.state_dim = config.state_dim
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self.obs_dim = config.obs_dim
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self.action_dim = config.action_dim
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# Initialize model parameters
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self.transition_matrix = initialize_transitions()
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self.observation_matrix = initialize_observations()
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self.preferences = initialize_preferences()
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def state_transition(self, state, action):
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"""Compute state transition probability."""
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return compute_transition_prob(
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state, action, self.transition_matrix
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)
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def observation_likelihood(self, state):
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"""Compute observation likelihood."""
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return compute_observation_prob(
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state, self.observation_matrix
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)
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def prior_preference(self, state):
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"""Compute prior preference."""
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return compute_preference(state, self.preferences)
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```
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### 2. Variational Inference
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```python
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class VariationalInference:
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"""
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Implements variational inference for belief updating.
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"""
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def __init__(self, model):
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self.model = model
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self.learning_rate = 0.1
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def update_beliefs(self, beliefs, observation):
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"""Update beliefs using variational inference."""
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# Compute free energy gradients
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gradients = compute_free_energy_gradients(
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beliefs, observation, self.model
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)
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# Update beliefs using gradient descent
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updated_beliefs = beliefs - self.learning_rate * gradients
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# Normalize beliefs
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return normalize_distribution(updated_beliefs)
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```
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### 3. Policy Selection
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```python
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class PolicySelection:
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"""
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Policy selection using expected free energy.
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"""
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def __init__(self, model):
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self.model = model
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self.temperature = 1.0
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def evaluate_policies(self, beliefs, policies):
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"""Evaluate policies using expected free energy."""
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G = np.zeros(len(policies))
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for i, policy in enumerate(policies):
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# Compute expected free energy components
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pragmatic_value = compute_pragmatic_value(
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beliefs, policy, self.model
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)
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epistemic_value = compute_epistemic_value(
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beliefs, policy, self.model
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)
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G[i] = pragmatic_value + epistemic_value
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return G
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def select_action(self, beliefs):
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"""Select action using softmax policy selection."""
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policies = generate_policies()
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G = self.evaluate_policies(beliefs, policies)
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# Softmax policy selection
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probabilities = softmax(G / self.temperature)
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return sample_action(probabilities, policies)
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```
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## Implementation Steps
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### 1. Basic Setup
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```python
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def setup_active_inference_agent(config):
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"""Setup basic active inference agent."""
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# Create generative model
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model = GenerativeModel(config)
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# Setup inference
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inference = VariationalInference(model)
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# Setup policy selection
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policy = PolicySelection(model)
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return ActiveInferenceAgent(model, inference, policy)
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```
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### 2. Main Agent Class
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```python
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class ActiveInferenceAgent:
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"""
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Complete active inference agent implementation.
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"""
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def __init__(self, model, inference, policy):
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self.model = model
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self.inference = inference
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self.policy = policy
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# Initialize beliefs
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self.beliefs = initialize_beliefs(model.state_dim)
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def step(self, observation):
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"""Single step of perception-action cycle."""
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# 1. Update beliefs
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self.beliefs = self.inference.update_beliefs(
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self.beliefs, observation
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)
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# 2. Select action
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action = self.policy.select_action(self.beliefs)
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# 3. Execute action
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return action
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```
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## Advanced Features
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### 1. Hierarchical Processing
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```python
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class HierarchicalAgent:
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"""
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Hierarchical active inference implementation.
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"""
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def __init__(self, level_configs):
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self.levels = [
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ActiveInferenceAgent(config)
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for config in level_configs
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]
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def update(self, observation):
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"""Update all levels."""
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for level in self.levels:
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prediction = level.step(observation)
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observation = prediction # Pass prediction as observation
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```
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### 2. Memory Integration
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```python
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class MemoryAugmentedAgent:
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"""
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Agent with memory integration.
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"""
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def __init__(self, config):
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super().__init__(config)
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self.memory = EpisodicMemory(config.memory_size)
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def step(self, observation):
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# Integrate memory into belief updating
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memory_state = self.memory.retrieve(self.beliefs)
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augmented_beliefs = integrate_memory(
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self.beliefs, memory_state
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)
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# Standard active inference step
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action = super().step(observation)
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# Update memory
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self.memory.store(self.beliefs, action, observation)
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return action
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```
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## Configuration Examples
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### Basic Configuration
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```yaml
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agent_config:
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state_dim: 10
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obs_dim: 5
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action_dim: 3
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learning:
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learning_rate: 0.1
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temperature: 1.0
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model:
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hidden_dims: [64, 32]
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activation: "relu"
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```
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### Hierarchical Configuration
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```yaml
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hierarchical_config:
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levels:
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- state_dim: 20
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temporal_scale: 1
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- state_dim: 10
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temporal_scale: 5
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- state_dim: 5
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temporal_scale: 10
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```
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## Testing and Validation
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### 1. Unit Tests
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```python
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def test_belief_updating():
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"""Test belief updating mechanism."""
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agent = setup_test_agent()
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initial_beliefs = agent.beliefs.copy()
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observation = generate_test_observation()
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agent.step(observation)
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assert np.all(agent.beliefs != initial_beliefs)
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assert is_normalized(agent.beliefs)
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```
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### 2. Integration Tests
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```python
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def test_complete_cycle():
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"""Test complete perception-action cycle."""
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agent = setup_test_agent()
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environment = setup_test_environment()
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observation = environment.reset()
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for _ in range(100):
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action = agent.step(observation)
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observation, reward, done, _ = environment.step(action)
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assert is_valid_action(action)
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if done:
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break
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```
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## Performance Optimization
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### 1. Efficient Computation
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```python
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@numba.jit(nopython=True)
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def compute_free_energy_gradients(beliefs, observation, model):
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"""Optimized gradient computation."""
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# Efficient implementation
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pass
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```
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### 2. Parallel Processing
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```python
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class ParallelPolicyEvaluation:
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"""Parallel policy evaluation."""
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def evaluate_policies(self, beliefs, policies):
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with concurrent.futures.ProcessPoolExecutor() as executor:
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G = list(executor.map(
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self._evaluate_single_policy,
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[(beliefs, p) for p in policies]
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))
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return np.array(G)
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```
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## Common Issues and Solutions
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### 1. Numerical Stability
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```python
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def stable_softmax(x):
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"""Numerically stable softmax implementation."""
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x = x - np.max(x) # Subtract maximum for stability
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exp_x = np.exp(x)
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return exp_x / np.sum(exp_x)
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```
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### 2. Belief Normalization
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```python
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def normalize_beliefs(beliefs, epsilon=1e-10):
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"""Safe belief normalization."""
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beliefs = np.clip(beliefs, epsilon, None)
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return beliefs / np.sum(beliefs)
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```
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## Usage Example
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```python
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# Setup agent
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config = load_config("agent_config.yaml")
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agent = setup_active_inference_agent(config)
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# Run simulation
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environment = setup_environment()
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observation = environment.reset()
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for step in range(max_steps):
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# Agent step
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action = agent.step(observation)
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# Environment step
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observation, reward, done, info = environment.step(action)
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# Logging and visualization
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log_step(step, agent, observation, reward)
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visualize_state(agent, environment)
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if done:
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break
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```
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## References
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- [[knowledge_base/cognitive/active_inference|Active Inference Theory]]
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- [[knowledge_base/mathematics/free_energy_theory|Free Energy Theory]]
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- [[knowledge_base/mathematics/variational_methods|Variational Methods]]
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- [[examples/active_inference_basic|Basic Example]] |