зеркало из
https://github.com/docxology/cognitive.git
synced 2025-11-01 05:36:05 +02:00
362 строки
9.2 KiB
Markdown
362 строки
9.2 KiB
Markdown
---
|
|
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]] |