📋 Document Metadata
Type: Integration Guide | Audience: Researchers, Developers | Complexity: Advanced
Cross-References: PoE-World Overview | GNN Advanced Patterns | LLM Integration
This document explores the integration of PoE-World (Products of Programmatic Experts for World Modeling) with GNN (Generalized Notation Notation) for Active Inference modeling. The synergy between these approaches enables:
- Compositional World Models: Express PoE-World's modular expert structure using GNN notation
- Active Inference Integration: Embed PoE-World dynamics within Active Inference frameworks
- LLM-Enhanced GNN: Leverage program synthesis for automated GNN model generation
- Interpretable AI: Combine the interpretability of both frameworks for explainable world modeling
🔗 Related Work: PoE-World builds on program synthesis and LLM-based code generation, areas where GNN can provide structured representation and validation capabilities.
PoE-World's hierarchical architecture maps naturally to GNN's factor-based representation:
Agent System (agents/) → Policy Factors (π_c0, u_c0)
Learning Framework (learners/) → Hidden State Factors (s_f0, s_f1, ...)
Environment Classes (classes/) → Observation Modalities (o_m0, o_m1, ...)
Configuration Management (conf/) → GNN Model Parameters
The PoE-World Agent class orchestrates three main phases that correspond to GNN factors:
# PoE-World Agent Structure
class Agent:
def __init__(self, config: Dict[str, Any], world_learner: WorldModelLearner):
self.world_learner = world_learner # → s_f_world_knowledge
self.mcts = MCTS(config) # → π_c0 planning_policy
self.abstract_planning = True # → s_f_planning_modeGNN Representation:
### Agent System Factors
s_f_world_knowledge[world_size, 1, type=categorical] ### PoE-World WorldModelLearner state
s_f_planning_mode[2, 1, type=categorical] ### Abstract vs concrete planning
s_f_mcts_state[tree_depth, 1, type=categorical] ### Current MCTS search state
π_c0[action_space, 1, type=continuous] ### MCTS-derived policy
PoE-World implements the product of experts as:
# From learners/synthesizer.py - Expert combination
class PoEWorldLearner(WorldModelLearner):
def __init__(self, config: DictConfig):
self.obj_model_learners: Dict[str, ObjModelLearner] = {} # Individual experts
self.all_obj_types: Optional[List[str]] = None # Expert categoriesThis maps to GNN as hierarchical Active Inference where:
- Expert Learners (
obj_model_learners) → Specialized State Factors (s_f_expert_i) - Object Types (
all_obj_types) → Expert Categories in state space - Model Composition → GNN Connection Patterns between factors
## GNNVersionAndFlags
GNN v1.1
ProcessingFlags: active_inference, program_synthesis, llm_enhanced, mcts_planning
## ModelName
PoEWorld_Technical_Integration_Model
## ModelAnnotation
Technical integration of PoE-World's actual implementation with Active Inference.
Maps PoE-World's Agent, MCTS, WorldModelLearner, and Synthesizer classes to GNN factors.
Implements the full program synthesis pipeline within Active Inference framework.
## StateSpaceBlock
### Core PoE-World Architecture Factors
s_f0[agent_configs, 1, type=categorical] ### Agent configuration states
s_f1[world_model_size, 1, type=categorical] ### WorldModelLearner state
s_f2[mcts_tree_size, 1, type=categorical] ### MCTS search tree state
s_f3[synthesis_context, 1, type=categorical] ### LLM synthesis context
### Object-Specific Expert Factors (from obj_model_learners)
s_f10[num_obj_types, 1, type=categorical] ### Object type classifications
s_f11[expert_programs, 1, type=categorical] ### Synthesized expert programs
s_f12[expert_weights, 1, type=continuous] ### θ_i weights per expert
s_f13[constraint_graph, 1, type=categorical] ### Spatial relationship constraints
### Synthesizer-Specific Factors
s_f20[action_patterns, 1, type=categorical] ### ActionSynthesizer state
s_f21[movement_patterns, 1, type=categorical] ### PassiveMovementSynthesizer
s_f22[velocity_patterns, 1, type=categorical] ### VelocitySynthesizer
s_f23[constraint_patterns, 1, type=categorical] ### ConstraintsSynthesizer
s_f24[multistep_context, 1, type=categorical] ### MultiTimestep synthesizers
### Environment and State Representation
s_f30[concrete_states, 1, type=categorical] ### Full object-level states
s_f31[abstract_states, 1, type=categorical] ### Compressed state representations
s_f32[obj_memory, 1, type=categorical] ### ObjListWithMemory state
s_f33[interaction_state, 1, type=categorical] ### Object interaction patterns
### MCTS Planning Factors
s_f40[search_budget, 1, type=continuous] ### MCTS iteration budget
s_f41[beam_states, 1, type=categorical] ### Beam search current states
s_f42[path_evaluations, 1, type=continuous] ### Heuristic path evaluations
s_f43[target_states, 1, type=categorical] ### Planning target states
### Observations (OCAtari Integration)
o_m0[visual_obs, 1, type=categorical] ### Raw Atari visual observations
o_m1[object_states, 1, type=categorical] ### OCAtari object-centric obs
o_m2[state_transitions, 1, type=categorical] ### StateTransitionTriplet obs
o_m3[synthesis_feedback, 1, type=categorical] ### LLM synthesis results
### Actions/Policies
u_c0[atari_actions, 1, type=categorical] ### Atari action space (up/down/left/right)
π_c0[mcts_policy, 1, type=continuous] ### MCTS-derived action policy
π_c1[abstract_plan, 1, type=categorical] ### High-level symbolic plan
π_c2[synthesis_strategy, 1, type=categorical] ### Expert synthesis policy
## Connections
### Core PoE-World Architecture Flow
s_f0 > s_f1 ### Agent config influences world model
s_f1 > s_f2 ### World model guides MCTS
s_f2 > π_c0 ### MCTS produces action policy
s_f3 > s_f11 ### Synthesis context generates experts
### Expert Learning and Composition
s_f10 > s_f20, s_f21, s_f22, s_f23 ### Object types determine synthesizer activation
s_f20, s_f21, s_f22, s_f23 > s_f11 ### Synthesizers generate expert programs
s_f11, s_f12 > s_f1 ### Weighted experts form world model
s_f24 > s_f11 ### Multi-timestep context enhances experts
### State Representation Hierarchy
s_f30 > s_f31 ### Concrete states compress to abstract
s_f32 > s_f30, s_f31 ### Memory provides temporal context
s_f33 > s_f13 ### Interactions generate constraints
### MCTS Planning Integration
s_f1, s_f31 > s_f40 ### World model and states set search budget
s_f40, s_f43 > s_f41 ### Budget and targets guide beam search
s_f41, s_f42 > π_c0, π_c1 ### Search evaluation produces policies
### Observation Processing Pipeline
o_m0 > o_m1 ### Visual obs to object-centric
o_m1 > o_m2 ### Objects to state transitions
o_m2 > s_f3 ### Transitions inform synthesis
o_m3 > s_f11, s_f12 ### Synthesis results update experts
### Policy Execution
π_c1 > π_c0 ### Abstract plans guide concrete policy
π_c0 > u_c0 ### Policy determines actions
π_c2 > s_f3 ### Synthesis strategy updates context
## InitialParameterization
### PoE-World Configuration Parameters
agent_budget_iterations = 2000 ### MCTS search budget
synthesis_temperature = 0.1 ### LLM generation temperature
beam_search_width = 10 ### Parallel search beam width
expert_weight_learning_rate = 0.01 ### θ_i update rate
### Synthesizer-Specific Parameters
action_synthesis_context_length = 5 ### ActionSynthesizer history window
multistep_synthesis_horizon = 10 ### MultiTimestep analysis window
constraint_spatial_threshold = 0.8 ### Spatial relationship threshold
velocity_pattern_min_length = 3 ### Minimum velocity pattern length
### MCTS Configuration
mcts_exploration_constant = 1.4 ### UCB exploration parameter
mcts_simulation_depth = 50 ### Maximum simulation rollout depth
mcts_parallel_searches = 8 ### Concurrent search processes
abstract_planning_threshold = 0.7 ### When to use abstract vs concrete planning
### World Model Parameters
world_model_cache_enabled = true ### Enable prediction caching
world_model_update_frequency = 10 ### Update frequency (timesteps)
expert_combination_method = "weighted" ### Product of experts combination
constraint_violation_penalty = -10.0 ### Penalty for constraint violations
## Equations
### Expert Synthesis with Technical Implementation
```latex
\text{Expert}_i = \text{LLM}(\text{StateTransitionTriplet}, \text{SynthesisContext}_i)
V(s) = \frac{1}{N(s)} \sum_{i=1}^{N(s)} R_i + \gamma \max_a Q(s,a)P(o_{t+1}|o_{1:t}, a_{1:t}) = \frac{1}{Z} \prod_{i \in \text{obj\_model\_learners}} [P_i^{\text{expert}}(o_{t+1}|o_{1:t}, a_{1:t})]^{\theta_i}F = D_{KL}[q(s_t|o_{1:t}) || p(s_t)] - E_q[\ln \prod_i P_i^{\text{expert}}(o_t|s_t)^{\theta_i}]\dot{\theta}_i = -\frac{\partial F}{\partial \theta_i} = E_q[\ln P_i^{\text{expert}}(o_t|s_t)] - \lambda(\theta_i - \theta_i^{\text{prior}})\text{AbstractState}(s) = \text{Compress}(s) \text{ s.t. } \bigwedge_{c \in \text{constraints}} c(s) = \text{True}Dynamic
DiscreteTime = t
ModelTimeHorizon = 200 ### Extended horizon for strategic planning
ExpertUpdateFrequency = 10Hz ### Expert weight updates
PlanningFrequency = 1Hz ### MCTS planning updates
SynthesisUpdateFrequency = 0.1Hz ### LLM expert synthesis frequency
WorldModelCacheRefresh = 100 ### Cache refresh interval (timesteps)
s_f0 = AgentConfigurationFactor s_f1 = WorldModelLearnerFactor s_f2 = MCTSSearchTreeFactor s_f3 = LLMSynthesisContextFactor
s_f10 = ObjectTypeClassificationFactor s_f11 = ExpertProgramRepositoryFactor s_f12 = ExpertWeightParameterFactor s_f13 = SpatialConstraintGraphFactor
s_f20 = ActionSynthesizerStateFactor s_f21 = PassiveMovementSynthesizerFactor s_f22 = VelocitySynthesizerFactor s_f23 = ConstraintsSynthesizerFactor s_f24 = MultiTimestepSynthesisContextFactor
s_f30 = ConcreteStateRepresentationFactor s_f31 = AbstractStateRepresentationFactor s_f32 = TemporalMemoryBufferFactor s_f33 = ObjectInteractionPatternFactor
s_f40 = SearchBudgetAllocationFactor s_f41 = BeamSearchStateFactor s_f42 = HeuristicEvaluationFactor s_f43 = PlanningTargetStateFactor
o_m0 = RawVisualObservationModality
o_m1 = ObjectCentricObservationModality
o_m2 = StateTransitionTripletModality
o_m3 = SynthesisFeedbackModality
u_c0 = AtariActionSpaceFactor π_c0 = MCTSActionPolicyFactor π_c1 = AbstractSymbolicPlanFactor π_c2 = ExpertSynthesisStrategyFactor
A_m0 = VisualObservationLikelihoodMatrix A_m1 = ObjectCentricObservationMatrix A_m2 = StateTransitionLikelihoodMatrix B_f1 = WorldModelTransitionMatrix B_f2 = MCTSSearchTransitionMatrix C_m0 = VisualCoherencePreferences C_m1 = ObjectPersistencePreferences D_f1 = ExpertWeightPriorDistribution
Created: 2025-01-15
LastModified: 2025-01-15
Version: 2.0 - Technical Integration
Integration: PoE-World Technical Architecture + GNN + Active Inference
Implementation: Based on actual PoE-World codebase structure
ModelCreator: GNN Documentation Team Institution: Active Inference Institute ResearchContext: Technical Integration of Compositional World Modeling with Program Synthesis License: MIT TechnicalBasis: PoE-World GitHub Repository Analysis
## Technical Implementation Strategy
### 1. PoE-World to GNN Translation Pipeline
**Agent System Translation**:
```python
def translate_poe_agent_to_gnn(agent: Agent, config: DictConfig) -> GNNModel:
"""Convert PoE-World Agent to GNN factor representation"""
# Extract core agent components
world_learner_state = agent.world_learner.get_state()
mcts_state = agent.mcts.get_current_tree_state()
config_params = agent.config
# Create GNN factors
gnn_factors = {
's_f0': create_config_factor(config_params),
's_f1': create_world_model_factor(world_learner_state),
's_f2': create_mcts_factor(mcts_state),
's_f3': create_synthesis_context_factor(agent.world_learner.synthesis_context)
}
# Map expert learners to specialized factors
for obj_type, learner in agent.world_learner.obj_model_learners.items():
factor_id = f's_f_expert_{obj_type}'
gnn_factors[factor_id] = create_expert_factor(learner)
return GNNModel(factors=gnn_factors, connections=infer_connections(gnn_factors))
World Model Learner Integration:
class GNNPoEWorldLearner(PoEWorldLearner):
"""Extended PoE-World learner with GNN integration"""
def __init__(self, config: DictConfig, gnn_model: GNNModel):
super().__init__(config)
self.gnn_model = gnn_model
self.active_inference_updater = ActiveInferenceUpdater(gnn_model)
def update_expert_weights_with_active_inference(self, observations: List[Any]):
"""Update expert weights using Active Inference principles"""
# Convert PoE-World observations to GNN observation format
gnn_observations = self.convert_to_gnn_observations(observations)
# Compute prediction errors for each expert
expert_predictions = {}
for obj_type, learner in self.obj_model_learners.items():
predictions = learner.predict(observations)
expert_predictions[obj_type] = predictions
# Update weights through Active Inference free energy minimization
weight_updates = self.active_inference_updater.compute_weight_updates(
gnn_observations, expert_predictions, self.current_expert_weights
)
# Apply updates to PoE-World expert weights
self.update_expert_weights(weight_updates)
# Update GNN model factors
self.gnn_model.update_expert_factors(weight_updates)LLM Synthesizer to GNN Factor Translation:
class GNNSynthesizerIntegration:
"""Integration layer between PoE-World synthesizers and GNN"""
def __init__(self, synthesizers: Dict[str, Synthesizer], gnn_model: GNNModel):
self.synthesizers = synthesizers
self.gnn_model = gnn_model
self.synthesis_factors = self._create_synthesis_factors()
def _create_synthesis_factors(self) -> Dict[str, GNNFactor]:
"""Create GNN factors for each synthesizer type"""
factors = {}
for synth_name, synthesizer in self.synthesizers.items():
factor_id = f's_f_{synth_name}_synthesizer'
factors[factor_id] = GNNFactor(
name=factor_id,
dimensions=self._infer_synthesizer_dimensions(synthesizer),
factor_type='categorical',
description=f'State factor for {synth_name} synthesizer'
)
return factors
async def synthesize_with_gnn_context(self, state_transitions: List[StateTransitionTriplet],
gnn_context: GNNContext) -> List[str]:
"""Synthesize expert programs using GNN context"""
# Extract relevant GNN state for synthesis context
synthesis_context = self.gnn_model.get_synthesis_context(gnn_context)
# Run synthesizers with enhanced context
synthesized_programs = []
for synth_name, synthesizer in self.synthesizers.items():
if self._should_activate_synthesizer(synth_name, synthesis_context):
programs = await synthesizer.a_synthesize(
state_transitions,
gnn_context=synthesis_context
)
synthesized_programs.extend(programs)
# Update GNN synthesis factors
self._update_synthesis_factors(synthesized_programs)
return synthesized_programsEnhanced MCTS with GNN Planning:
class GNNEnhancedMCTS(MCTS):
"""MCTS with GNN Active Inference integration"""
def __init__(self, config: DictConfig, gnn_model: GNNModel):
super().__init__(config)
self.gnn_model = gnn_model
self.active_inference_planner = ActiveInferencePlanner(gnn_model)
def search_with_active_inference(self, cur_obj_list: ObjListWithMemory,
target_abstract_state: str,
world_model: WorldModel,
iterations: int = 2000) -> List[str]:
"""MCTS search enhanced with Active Inference planning"""
# Convert current state to GNN representation
gnn_state = self.convert_to_gnn_state(cur_obj_list)
# Use Active Inference for initial policy prior
prior_policy = self.active_inference_planner.compute_policy_prior(
gnn_state, target_abstract_state
)
# Enhanced MCTS with Active Inference guidance
for iteration in range(iterations):
# Selection with Active Inference bias
node = self.select_with_ai_bias(self.root, prior_policy)
# Expansion using world model and GNN predictions
if not node.is_terminal():
gnn_transitions = self.gnn_model.predict_transitions(node.state)
self.expand_with_gnn_predictions(node, gnn_transitions)
# Simulation with world model validation
reward = self.simulate_with_world_model_validation(node, world_model)
# Backpropagation with Active Inference updates
self.backpropagate_with_ai_updates(node, reward)
# Update GNN planning factors
self.gnn_model.update_planning_factors(iteration, node, reward)
return self.extract_best_action_sequence()OCAtari to GNN Observation Conversion:
class OCAtariGNNConverter:
"""Convert OCAtari observations to GNN-compatible format"""
def __init__(self, gnn_model: GNNModel):
self.gnn_model = gnn_model
self.observation_mapping = self._create_observation_mapping()
def convert_obj_list_to_gnn(self, obj_list: ObjList) -> Dict[str, np.ndarray]:
"""Convert OCAtari ObjList to GNN observation format"""
gnn_observations = {}
# Visual observations (o_m0)
gnn_observations['o_m0'] = self._extract_visual_features(obj_list)
# Object-centric observations (o_m1)
gnn_observations['o_m1'] = self._extract_object_features(obj_list)
# State transition observations (o_m2)
if hasattr(obj_list, 'history'):
gnn_observations['o_m2'] = self._extract_transition_features(obj_list)
return gnn_observations
def convert_state_transition_triplet(self, triplet: StateTransitionTriplet) -> Dict[str, Any]:
"""Convert state transition triplet to GNN format"""
return {
'input_state': self.convert_obj_list_to_gnn(triplet.input_state),
'action': self._encode_action(triplet.event),
'output_state': self.convert_obj_list_to_gnn(triplet.output_state),
'transition_metadata': {
'timestamp': triplet.timestamp if hasattr(triplet, 'timestamp') else None,
'validity': self._validate_transition(triplet)
}
}Montezuma's Revenge Integration:
### Montezuma's Revenge Specific Factors
s_f_room_navigation[num_rooms, 1, type=categorical] ### Room-based navigation expert
s_f_key_collection[num_keys, 1, type=categorical] ### Key collection strategy expert
s_f_enemy_avoidance[num_enemy_types, 1, type=categorical] ### Enemy pattern recognition expert
s_f_ladder_climbing[ladder_positions, 1, type=categorical] ### Ladder navigation expert
s_f_rope_swinging[rope_positions, 1, type=categorical] ### Rope physics expert
### Technical Mapping
s_f_room_navigation ↔ ActionSynthesizer(room_transitions)
s_f_key_collection ↔ MultiTimestepActionSynthesizer(key_sequences)
s_f_enemy_avoidance ↔ VelocitySynthesizer(enemy_patterns)
s_f_ladder_climbing ↔ PassiveMovementSynthesizer(vertical_movement)
s_f_rope_swinging ↔ ConstraintsSynthesizer(physics_constraints)
Pong Environment Integration:
### Pong Specific Factors
s_f_paddle_control[paddle_positions, 1, type=continuous] ### Paddle movement expert
s_f_ball_tracking[ball_trajectory, 1, type=continuous] ### Ball prediction expert
s_f_opponent_modeling[opponent_strategy, 1, type=categorical] ### Opponent behavior expert
s_f_angle_optimization[hit_angles, 1, type=continuous] ### Shot angle expert
### Technical Implementation
s_f_paddle_control ↔ VelocitySynthesizer(paddle_dynamics)
s_f_ball_tracking ↔ MultiTimestepVelocitySynthesizer(ball_physics)
s_f_opponent_modeling ↔ ActionSynthesizer(opponent_patterns)
s_f_angle_optimization ↔ ConstraintsSynthesizer(physics_optimization)
Hydra Config to GNN Translation:
def generate_gnn_from_poe_config(config_path: str) -> GNNModel:
"""Generate GNN model from PoE-World Hydra configuration"""
# Load PoE-World configuration
with initialize(config_path="conf"):
config = compose(config_name=config_path)
# Extract relevant parameters
task_name = config.task
agent_params = config.agent
synthesis_params = config.synthesis
# Create task-specific GNN factors
task_factors = create_task_specific_factors(task_name)
agent_factors = create_agent_factors(agent_params)
synthesis_factors = create_synthesis_factors(synthesis_params)
# Combine into complete GNN model
all_factors = {**task_factors, **agent_factors, **synthesis_factors}
connections = infer_factor_connections(all_factors, config)
return GNNModel(
name=f"PoEWorld_{task_name}_GNN",
factors=all_factors,
connections=connections,
parameters=extract_gnn_parameters(config)
)SLURM Job Management with GNN:
class GNNParallelProcessor:
"""Integrate PoE-World's parallel processing with GNN updates"""
def __init__(self, gnn_model: GNNModel, slurm_config: Dict[str, Any]):
self.gnn_model = gnn_model
self.slurm_config = slurm_config
self.job_queue = SLURMJobQueue(slurm_config)
self.gnn_updater = ParallelGNNUpdater(gnn_model)
def parallel_expert_synthesis(self, state_transitions: List[StateTransitionTriplet]) -> Dict[str, List[str]]:
"""Parallelize expert synthesis across compute nodes with GNN coordination"""
# Partition synthesis tasks
synthesis_jobs = self.partition_synthesis_tasks(state_transitions)
# Submit parallel jobs with GNN context
job_ids = []
for job_data in synthesis_jobs:
gnn_context = self.gnn_model.get_synthesis_context()
job_id = self.job_queue.submit_synthesis_job(job_data, gnn_context)
job_ids.append(job_id)
# Collect results and update GNN
synthesis_results = {}
for job_id in job_ids:
result = self.job_queue.wait_for_completion(job_id)
synthesis_results[job_id] = result
# Update GNN factors with parallel results
self.gnn_updater.update_from_parallel_result(result)
return synthesis_results- GNN provides formal validation for synthesized expert programs
- Type checking ensures expert compatibility and composition
- Standardized representation enables cross-environment transfer
- Principled expert weight learning through free energy minimization
- Uncertainty quantification in expert predictions and compositions
- Bayesian model selection for optimal expert combinations
- GNN factor representation scales with expert complexity
- Parallel processing integrates naturally with GNN update mechanisms
- Configuration management through structured GNN parameters
- Explicit expert structure visible in GNN factor graph
- Mathematical foundations enable analysis and debugging
- Traceable decision paths through factor connections
class PoEWorldGNNIntegrationTests:
"""Comprehensive test suite for PoE-World GNN integration"""
def test_agent_to_gnn_conversion(self):
"""Test Agent class to GNN factor conversion"""
agent = create_test_agent()
gnn_model = translate_poe_agent_to_gnn(agent, test_config)
assert validate_gnn_structure(gnn_model)
assert verify_factor_mappings(agent, gnn_model)
def test_synthesizer_integration(self):
"""Test synthesizer system with GNN context"""
synthesizers = create_test_synthesizers()
gnn_integration = GNNSynthesizerIntegration(synthesizers, test_gnn_model)
state_transitions = generate_test_transitions()
results = gnn_integration.synthesize_with_gnn_context(state_transitions, test_context)
assert len(results) > 0
assert all(validate_expert_program(prog) for prog in results)
def test_mcts_active_inference(self):
"""Test MCTS with Active Inference integration"""
mcts = GNNEnhancedMCTS(test_config, test_gnn_model)
obj_list = create_test_obj_list()
action_sequence = mcts.search_with_active_inference(
obj_list, target_state="goal", world_model=test_world_model
)
assert len(action_sequence) > 0
assert validate_action_sequence(action_sequence)- Complete Agent class to GNN factor conversion
- Implement synthesizer system with GNN context integration
- Validate on Montezuma's Revenge and Pong environments
- Create automated testing suite for integration components
- Full MCTS Active Inference integration with planning factors
- Hierarchical expert composition using GNN factor hierarchies
- Parallel processing optimization with distributed GNN updates
- Cross-environment expert transfer using GNN templates
- Automated GNN generation from PoE-World experimental results
- Real-world robotics applications with physical constraints
- Large-scale compositional world models for complex environments
- Integration with other Active Inference frameworks (RxInfer, PyMDP)
Related Documentation:
- PoE-World Research Overview
- GNN Advanced Modeling Patterns
- LLM Integration Guide
- Program Synthesis with GNN
- Hierarchical Templates
- MCTS Integration Patterns
Technical References:
- PoE-World GitHub Repository: https://github.com/topwasu/poe-world
- PoE-World Agent Implementation:
agents/agent.py - MCTS Implementation:
agents/mcts.py - World Model Learner:
learners/world_model_learner.py - Synthesizer System:
learners/synthesizer.py
Status: Technical Integration Guide
Implementation Status: Architecture Complete, Implementation In Progress
Cross-Reference Network: ✅ Integrated with GNN Documentation Ecosystem