locsec/cognitive
1
Форкнуть 0
зеркало из https://github.com/docxology/cognitive.git synced 2025-10-30 12:46:04 +02:00
Код Задачи Пакеты Проекты Релизы Вики Активность
cognitive/docs/guides/learning_paths/pomdp_framework.md
Daniel Ari Friedman a61f13a26f Updates
2025-02-07 11:08:25 -08:00

5.9 KiB
Исходник Ответственный История

title type status created tags semantic_relations
POMDP Framework Learning Path learning_path stable 2024-02-07
pomdp
active_inference
learning
type links
implements
learning_path_template
type links
relates
knowledge_base/agents/GenericPOMDP/README
knowledge_base/cognitive/active_inference

POMDP Framework Learning Path

Overview

This learning path guides you through understanding and implementing Partially Observable Markov Decision Processes (POMDPs), with special focus on their application in active inference. You'll learn the theoretical foundations, mathematical principles, and practical implementations.

Prerequisites

Required Knowledge

Recommended Background

  • Python programming
  • Basic reinforcement learning
  • Linear algebra

Learning Progression

1. POMDP Foundations (Week 1-2)

Core Concepts

Practical Exercises

Learning Objectives

  • Understand POMDP fundamentals
  • Implement belief state updates
  • Master policy evaluation

2. Active Inference Integration (Week 3-4)

Advanced Concepts

Implementation Practice

Learning Objectives

  • Integrate active inference with POMDPs
  • Implement free energy minimization
  • Develop policy selection mechanisms

3. Advanced Implementation (Week 5-6)

Core Components

Projects

Learning Objectives

  • Implement hierarchical models
  • Develop multi-agent systems
  • Master advanced POMDP concepts

Implementation Examples

Basic POMDP

class POMDPAgent:
    def __init__(self, config):
        self.belief_state = initialize_belief_state()
        self.transition_model = create_transition_model()
        self.observation_model = create_observation_model()
        
    def update_belief(self, observation):
        """Update belief state using Bayes rule."""
        self.belief_state = bayes_update(
            self.belief_state,
            observation,
            self.observation_model
        )
        
    def select_action(self):
        """Select action using current belief state."""
        return policy_selection(self.belief_state)

Active Inference POMDP

class ActiveInferencePOMDP:
    def __init__(self, config):
        self.belief_state = initialize_belief_state()
        self.generative_model = create_generative_model()
        
    def update(self, observation):
        """Update using variational inference."""
        self.belief_state = variational_update(
            self.belief_state,
            observation,
            self.generative_model
        )
        
    def select_action(self):
        """Select action using expected free energy."""
        policies = generate_policies()
        G = compute_expected_free_energy(
            self.belief_state,
            policies,
            self.generative_model
        )
        return select_policy(G)

Study Resources

Core Reading

Code Examples

Additional Resources

  • Research papers
  • Tutorial notebooks
  • Video lectures

Assessment

Knowledge Checkpoints

  1. POMDP fundamentals
  2. Active inference integration
  3. Advanced implementations
  4. Real-world applications

Projects

  1. Mini-project: Basic POMDP implementation
  2. Integration: Active inference POMDP
  3. Final project: Complex application

Success Criteria

  • Working POMDP implementation
  • Active inference integration
  • Advanced model development
  • Application deployment

Next Steps

Advanced Paths

Specializations

Related Paths

Prerequisites

Follow-up Paths

Common Challenges

Theoretical Challenges

  • Understanding belief state updates
  • Grasping policy evaluation
  • Integrating active inference

Implementation Challenges

  • Efficient belief updates
  • Policy optimization
  • Scalability issues

Solutions

  • Start with simple examples
  • Use provided templates
  • Progressive complexity
  • Regular testing and validation