Daniel Ari Friedman a61f13a26f Updates
2025-02-07 11:08:25 -08:00

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---
title: Examples Index
type: index
status: stable
created: 2024-02-07
tags:
- examples
- implementation
- index
semantic_relations:
- type: organizes
links:
- [[implementation_examples]]
- [[usage_examples]]
---
# Examples Index
## Core Examples
### Active Inference Examples
- [[examples/active_inference/basic|Basic Active Inference]]
- [[examples/active_inference/hierarchical|Hierarchical Active Inference]]
- [[examples/active_inference/multi_agent|Multi-Agent Active Inference]]
### POMDP Examples
- [[examples/pomdp/basic|Basic POMDP]]
- [[examples/pomdp/belief_updating|Belief Updating]]
- [[examples/pomdp/policy_selection|Policy Selection]]
### Swarm Intelligence Examples
- [[examples/swarm/ant_colony|Ant Colony Simulation]]
- [[examples/swarm/particle_swarm|Particle Swarm]]
- [[examples/swarm/flocking|Flocking Behavior]]
## Implementation Examples
### Agent Implementation
```python
# Basic active inference agent
class ActiveInferenceAgent:
def __init__(self, config):
self.beliefs = initialize_beliefs()
self.model = create_generative_model()
def update(self, observation):
# Update beliefs using variational inference
self.beliefs = update_beliefs(
self.beliefs, observation, self.model
)
# Select action using expected free energy
action = select_action(self.beliefs, self.model)
return action
```
### Environment Implementation
```python
# Basic environment setup
class Environment:
def __init__(self, config):
self.state = initialize_state()
self.agents = create_agents()
def step(self, actions):
# Update environment state
self.state = update_state(self.state, actions)
# Generate observations
observations = generate_observations(self.state)
return observations
```
### Simulation Implementation
```python
# Basic simulation loop
def run_simulation(config):
env = Environment(config)
agent = ActiveInferenceAgent(config)
for step in range(config.max_steps):
# Agent-environment interaction
observation = env.get_observation()
action = agent.update(observation)
env.step(action)
```
## Advanced Examples
### Hierarchical Systems
- [[examples/hierarchical/perception|Hierarchical Perception]]
- [[examples/hierarchical/control|Hierarchical Control]]
- [[examples/hierarchical/learning|Hierarchical Learning]]
### Multi-Agent Systems
- [[examples/multi_agent/coordination|Agent Coordination]]
- [[examples/multi_agent/communication|Agent Communication]]
- [[examples/multi_agent/learning|Collective Learning]]
### Complex Systems
- [[examples/complex/emergence|Emergence Patterns]]
- [[examples/complex/adaptation|System Adaptation]]
- [[examples/complex/evolution|System Evolution]]
## Application Examples
### Robotics Applications
- [[examples/robotics/control|Robot Control]]
- [[examples/robotics/navigation|Robot Navigation]]
- [[examples/robotics/manipulation|Robot Manipulation]]
### Cognitive Applications
- [[examples/cognitive/learning|Learning Systems]]
- [[examples/cognitive/memory|Memory Systems]]
- [[examples/cognitive/attention|Attention Systems]]
### Biological Applications
- [[examples/biological/neural|Neural Systems]]
- [[examples/biological/collective|Collective Behavior]]
- [[examples/biological/adaptation|Adaptive Behavior]]
## Integration Examples
### Framework Integration
- [[examples/integration/pytorch|PyTorch Integration]]
- [[examples/integration/tensorflow|TensorFlow Integration]]
- [[examples/integration/jax|JAX Integration]]
### Tool Integration
- [[examples/tools/visualization|Visualization Tools]]
- [[examples/tools/analysis|Analysis Tools]]
- [[examples/tools/profiling|Profiling Tools]]
### System Integration
- [[examples/systems/environment|Environment Integration]]
- [[examples/systems/hardware|Hardware Integration]]
- [[examples/systems/distributed|Distributed Systems]]
## Testing Examples
### Unit Tests
```python
def test_belief_updating():
"""Test belief updating mechanism."""
agent = setup_test_agent()
observation = generate_test_observation()
initial_beliefs = agent.beliefs.copy()
agent.update(observation)
assert not np.allclose(agent.beliefs, initial_beliefs)
assert is_normalized(agent.beliefs)
```
### Integration Tests
```python
def test_agent_environment():
"""Test agent-environment interaction."""
env = setup_test_environment()
agent = setup_test_agent()
observation = env.reset()
for _ in range(100):
action = agent.update(observation)
observation, reward, done = env.step(action)
if done:
break
```
### Performance Tests
```python
def test_performance():
"""Test system performance."""
env = setup_benchmark_environment()
agent = setup_benchmark_agent()
start_time = time.time()
run_benchmark(env, agent)
end_time = time.time()
assert end_time - start_time < MAX_TIME
```
## Related Resources
### Documentation
- [[docs/guides/implementation_guides|Implementation Guides]]
- [[docs/api/implementation_api|Implementation API]]
- [[docs/research/implementation_research|Implementation Research]]
### Knowledge Base
- [[knowledge_base/cognitive/implementation_concepts|Implementation Concepts]]
- [[knowledge_base/mathematics/implementation_math|Implementation Mathematics]]
- [[knowledge_base/agents/implementation_patterns|Implementation Patterns]]
### Learning Resources
- [[learning_paths/implementation|Implementation Learning Path]]
- [[tutorials/implementation|Implementation Tutorials]]
- [[guides/implementation/best_practices|Implementation Best Practices]]