| title |
type |
status |
created |
tags |
semantic_relations |
| Examples Index |
index |
stable |
2024-02-07 |
| examples |
| implementation |
| index |
|
|
Examples Index
Core Examples
Active Inference Examples
POMDP Examples
Swarm Intelligence Examples
Implementation Examples
Agent Implementation
# 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
# 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
# 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
Multi-Agent Systems
Complex Systems
Application Examples
Robotics Applications
Cognitive Applications
Biological Applications
Integration Examples
Framework Integration
Tool Integration
System Integration
Testing Examples
Unit Tests
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
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
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
Knowledge Base
Learning Resources
