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cognitive/docs/guides/learning_paths/active_inference.md
Daniel Ari Friedman a61f13a26f Updates
2025-02-07 11:08:25 -08:00

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Исходник Ответственный История

title type status created tags semantic_relations
Active Inference Learning Path learning_path stable 2024-02-07
active_inference
learning
progression
type links
implements
learning_path_template
type links
relates
knowledge_base/cognitive/active_inference
knowledge_base/mathematics/free_energy_theory

Active Inference Learning Path

Overview

This learning path guides you through understanding and implementing active inference, from foundational concepts to advanced applications. You'll learn the theoretical principles, mathematical foundations, and practical implementations.

Prerequisites

Required Knowledge

Recommended Background

Learning Progression

1. Foundation (Week 1-2)

Core Concepts

Practical Exercises

Learning Objectives

  • Understand the free energy principle
  • Grasp predictive processing fundamentals
  • Implement basic belief updating

2. Mathematical Framework (Week 3-4)

Advanced Concepts

Implementation Practice

Learning Objectives

  • Master variational inference
  • Implement free energy computation
  • Understand expected free energy

3. Implementation (Week 5-6)

Core Components

Projects

Learning Objectives

  • Implement complete active inference agent
  • Master POMDP framework integration
  • Handle real-world applications

4. Advanced Topics (Week 7-8)

Specialized Areas

Advanced Projects

Learning Objectives

  • Implement hierarchical models
  • Develop multi-agent systems
  • Apply to complex domains

Study Resources

Core Reading

Code Examples

Additional Resources

  • Research papers collection
  • Video tutorials
  • Community discussions

Assessment

Knowledge Checkpoints

  1. Foundation: Free energy and predictive processing
  2. Mathematics: Variational methods and inference
  3. Implementation: Agent architecture and POMDP
  4. Advanced: Hierarchical and multi-agent systems

Projects

  1. Mini-project: Basic belief updating system
  2. Implementation: Active inference agent
  3. Final project: Complex application domain

Success Criteria

  • Theoretical understanding demonstrated
  • Working implementations completed
  • Advanced concepts mastered
  • Real-world application developed

Next Steps

Advanced Paths

Specializations

Related Paths

Prerequisites

Follow-up Paths

Implementation Examples

Basic Examples

# Basic active inference agent structure
class ActiveInferenceAgent:
    def __init__(self, model_params):
        self.beliefs = initialize_beliefs()
        self.policies = generate_policies()
        
    def update_beliefs(self, observation):
        # Belief updating using variational inference
        pass
        
    def select_action(self):
        # Policy selection using expected free energy
        pass

Advanced Implementation

# Hierarchical active inference
class HierarchicalAgent:
    def __init__(self, levels):
        self.levels = [
            ActiveInferenceAgent(level_params)
            for level_params in levels
        ]
        
    def update(self, observation):
        # Hierarchical message passing
        for level in self.levels:
            level.update_beliefs(observation)
            prediction = level.generate_prediction()
            observation = prediction  # For next level

Common Challenges

Theoretical Challenges

  • Understanding variational inference
  • Grasping hierarchical processing
  • Interpreting free energy

Implementation Challenges

  • Numerical stability
  • Performance optimization
  • Model design

Solutions

  • Start with simple examples
  • Use provided templates
  • Follow progressive complexity