| title |
type |
status |
created |
tags |
semantic_relations |
| Agents Index |
index |
stable |
2024-02-07 |
| agents |
| architectures |
| implementation |
|
|
Agents Index
Core Agent Types
Active Inference Agents
# Basic active inference agent
class ActiveInferenceAgent:
def __init__(self, config):
self.beliefs = initialize_beliefs(config)
self.model = create_generative_model(config)
def update(self, observation):
"""Update agent state."""
# Update beliefs
self.beliefs = update_beliefs(
self.beliefs,
observation,
self.model
)
# Select action
action = select_action(self.beliefs, self.model)
return action
POMDP Agents
# Basic POMDP agent
class POMDPAgent:
def __init__(self, config):
self.state_space = define_state_space(config)
self.action_space = define_action_space(config)
self.observation_model = create_observation_model(config)
self.transition_model = create_transition_model(config)
def update(self, observation):
"""Update agent state."""
# Update belief state
self.belief_state = update_belief_state(
self.belief_state,
observation,
self.observation_model
)
# Select action
action = select_policy(self.belief_state)
return action
Swarm Agents
# Basic swarm agent
class SwarmAgent:
def __init__(self, config):
self.position = initialize_position(config)
self.velocity = initialize_velocity(config)
self.sensors = create_sensors(config)
def update(self, neighbors, environment):
"""Update agent state."""
# Process sensor information
local_info = self.sensors.process(
neighbors,
environment
)
# Update movement
self.velocity = compute_velocity(local_info)
self.position += self.velocity
Agent Architectures
Hierarchical Agents
Memory-Based Agents
Learning Agents
Implementation Components
Core Components
# Belief state management
class BeliefState:
def __init__(self, config):
self.prior = initialize_prior(config)
self.likelihood = create_likelihood_model(config)
def update(self, observation):
"""Update beliefs using Bayes rule."""
posterior = bayes_update(
self.prior,
observation,
self.likelihood
)
self.prior = posterior
return posterior
# Policy selection
class PolicySelector:
def __init__(self, config):
self.policies = generate_policies(config)
self.value_function = create_value_function(config)
def select_action(self, belief_state):
"""Select action using policies."""
values = evaluate_policies(
self.policies,
belief_state,
self.value_function
)
return select_best_policy(values)
Advanced Features
# Hierarchical processing
class HierarchicalProcessor:
def __init__(self, config):
self.levels = create_hierarchy(config)
self.connections = initialize_connections(config)
def process(self, input_data):
"""Process input through hierarchy."""
# Bottom-up pass
for level in self.levels:
features = level.extract_features(input_data)
input_data = features
# Top-down pass
for level in reversed(self.levels):
predictions = level.generate_predictions()
level.update_state(predictions)
Integration Tools
# Environment integration
class EnvironmentInterface:
def __init__(self, config):
self.sensors = create_sensors(config)
self.actuators = create_actuators(config)
def observe(self, environment):
"""Get observations from environment."""
return self.sensors.process(environment)
def act(self, action):
"""Execute action in environment."""
return self.actuators.execute(action)
Example Implementations
Basic Examples
Advanced Examples
Integration Examples
Applications
Robotics
Cognitive Systems
Swarm Systems
Research Directions
Current Research
Open Questions
Related Resources
Documentation
Knowledge Base
Learning Resources
