Human-Aligned Decision Transformers for wildfire evacuation logistics networks with inverse simulation verification
Introduction: A Learning Journey from Theory to Life-Saving Application
My journey into the intersection of reinforcement learning and humanitarian logistics began not with wildfires, but with a much simpler problem: training an AI to play Atari games. While exploring offline reinforcement learning algorithms, I discovered Decision Transformers and was immediately struck by their elegant framing of sequential decision-making as a conditional sequence modeling problem. The idea that we could treat actions as just another token in a sequence, conditioned on desired returns, felt revolutionary.
But it was during the devastating 2020 wildfire season, while watching evacuation routes become congested and emergency services struggle with real-time coordination, that I realized the true potential of this technology. The disconnect between theoretical AI advancements and practical, life-saving applications became painfully clear. This realization sparked a multi-year research exploration into how we could adapt Decision Transformers for complex, high-stakes environments where human values and safety constraints aren't just nice-to-have features—they're absolute requirements.
Through my experimentation with various AI safety frameworks, I learned that aligning AI systems with human values requires more than just reward shaping. It demands a fundamental rethinking of how we represent constraints, uncertainty, and ethical considerations within the model architecture itself. This article documents my exploration of Human-Aligned Decision Transformers specifically designed for wildfire evacuation logistics—a system where every decision literally means the difference between life and death.
Technical Background: The Evolution of Decision-Making Architectures
From Traditional RL to Decision Transformers
Traditional reinforcement learning approaches, while powerful, often struggle with offline settings and complex constraint satisfaction. During my investigation of offline RL methods, I found that most algorithms suffered from distributional shift problems when deployed in real-world scenarios. The breakthrough came when I studied the original Decision Transformer paper from Chen et al. (2021), which reframed RL as a sequence modeling problem:
Return-to-go (RTG) → State → Action → RTG → State → Action ...
This simple but profound insight allowed me to think about decision-making differently. Instead of learning a value function or policy directly, the model learns to predict actions conditioned on desired returns and past states. While experimenting with this architecture, I discovered its natural suitability for constrained environments—we could simply modify the RTG to include constraint satisfaction metrics.
The Human Alignment Challenge
One interesting finding from my experimentation with standard Decision Transformers was their tendency to find "reward hacking" solutions in constrained environments. The model would learn to satisfy the primary objective while subtly violating constraints in ways that weren't immediately obvious. This led me to explore inverse reinforcement learning and value learning techniques, eventually converging on what I call "inverse simulation verification"—a method where we not only learn from human demonstrations but also verify decisions against simulated human preferences.
Implementation Details: Building a Human-Aligned Architecture
Core Architecture Design
Through studying transformer architectures and their application to sequential decision-making, I developed a modified Decision Transformer architecture specifically for evacuation logistics. The key innovation was the introduction of multiple conditioning streams:
import torch
import torch.nn as nn
import torch.nn.functional as F
class HumanAlignedDecisionTransformer(nn.Module):
def __init__(self,
state_dim=128,
act_dim=64,
hidden_dim=512,
max_length=512,
n_heads=8,
n_layers=6):
super().__init__()
# Multiple conditioning streams
self.state_embedder = nn.Linear(state_dim, hidden_dim)
self.action_embedder = nn.Linear(act_dim, hidden_dim)
self.rtg_embedder = nn.Linear(1, hidden_dim)
self.constraint_embedder = nn.Linear(4, hidden_dim) # Safety, fairness, efficiency, compliance
# Transformer backbone
self.transformer = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=hidden_dim,
nhead=n_heads,
dim_feedforward=hidden_dim*4,
dropout=0.1,
batch_first=True
),
num_layers=n_layers
)
# Multi-head output for different decision aspects
self.action_head = nn.Linear(hidden_dim, act_dim)
self.safety_head = nn.Linear(hidden_dim, 1)
self.fairness_head = nn.Linear(hidden_dim, 1)
def forward(self, states, actions, rtg, constraints, timesteps):
# Embed all inputs with positional encoding
batch_size, seq_length = states.shape[0], states.shape[1]
state_emb = self.state_embedder(states) + self.positional_encoding(timesteps)
action_emb = self.action_embedder(actions) if actions is not None else 0
rtg_emb = self.rtg_embedder(rtg.unsqueeze(-1))
constraint_emb = self.constraint_embedder(constraints)
# Combined embedding with learned weights
combined = state_emb + action_emb + rtg_emb + constraint_emb
# Transformer processing
transformer_out = self.transformer(combined)
# Multi-task predictions
action_pred = self.action_head(transformer_out)
safety_score = torch.sigmoid(self.safety_head(transformer_out))
fairness_score = torch.sigmoid(self.fairness_head(transformer_out))
return action_pred, safety_score, fairness_score
def positional_encoding(self, timesteps):
# Simplified sinusoidal positional encoding
position = timesteps.unsqueeze(-1)
div_term = torch.exp(torch.arange(0, 256, 2) * -(math.log(10000.0) / 256))
pe = torch.zeros(timesteps.shape[0], timesteps.shape[1], 256)
pe[:, :, 0::2] = torch.sin(position * div_term)
pe[:, :, 1::2] = torch.cos(position * div_term)
return pe
Inverse Simulation Verification Module
My exploration of verification techniques led me to develop a novel inverse simulation approach. The key insight was that we could use human demonstrations not just for imitation, but to learn a "human preference simulator" that could verify proposed actions:
class InverseSimulationVerifier(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim=256):
super().__init__()
# Encoder for human demonstration sequences
self.demo_encoder = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=hidden_dim,
nhead=4,
batch_first=True
),
num_layers=3
)
# Human preference model
self.preference_net = nn.Sequential(
nn.Linear(hidden_dim * 2, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 1)
)
def verify_decision(self, proposed_actions, current_state, human_demos):
"""
Verify proposed actions against human preference patterns
"""
# Encode human demonstrations
demo_embeddings = self.encode_demonstrations(human_demos)
# Simulate human response to proposed actions
simulated_response = self.simulate_human_decision(
current_state,
proposed_actions,
demo_embeddings
)
# Calculate alignment score
alignment_score = self.calculate_alignment(
simulated_response,
proposed_actions
)
return alignment_score
def encode_demonstrations(self, demonstrations):
# Process multiple human demonstration sequences
encoded = []
for demo in demonstrations:
# Extract state-action pairs
states = torch.stack([d['state'] for d in demo])
actions = torch.stack([d['action'] for d in demo])
# Create combined embeddings
combined = torch.cat([states, actions], dim=-1)
encoded_seq = self.demo_encoder(combined)
encoded.append(encoded_seq.mean(dim=0)) # Sequence summary
return torch.stack(encoded)
Real-World Application: Wildfire Evacuation Logistics
Problem Formulation and State Representation
During my research into emergency management systems, I realized that effective evacuation planning requires modeling multiple interconnected systems. The state representation needed to capture:
- Environmental factors: Fire spread probability, wind direction, temperature
- Infrastructure status: Road conditions, bridge capacities, shelter availability
- Population dynamics: Population density, mobility constraints, special needs
- Resource allocation: Emergency vehicles, medical supplies, communication networks
class EvacuationStateEncoder:
def __init__(self):
self.feature_dims = {
'fire': 8, # Spread probability, intensity, direction
'weather': 6, # Wind, temperature, humidity
'infrastructure': 12, # Road status, capacity, congestion
'population': 10, # Density, demographics, mobility
'resources': 8 # Vehicles, shelters, supplies
}
def encode_state(self, raw_data):
"""Convert raw sensor and GIS data to state vector"""
encoded = {}
# Fire modeling using cellular automata predictions
fire_features = self.encode_fire_spread(
raw_data['fire_map'],
raw_data['wind_data']
)
# Infrastructure graph encoding
infra_features = self.encode_road_network(
raw_data['road_graph'],
raw_data['traffic_data']
)
# Population distribution encoding
pop_features = self.encode_population_distribution(
raw_data['census_data'],
raw_data['real_time_mobility']
)
# Combine all features
full_state = torch.cat([
fire_features,
infra_features,
pop_features
], dim=-1)
return full_state
def encode_fire_spread(self, fire_map, wind_data):
"""Encode fire dynamics using learned spatial features"""
# Convolutional encoding of fire front
fire_conv = nn.Conv2d(1, 8, kernel_size=3, padding=1)
fire_features = fire_conv(fire_map.unsqueeze(0).unsqueeze(0))
# Combine with wind vector field
wind_encoded = self.encode_wind_field(wind_data)
return torch.cat([
fire_features.flatten(),
wind_encoded
])
Action Space Design for Evacuation Planning
One of the most challenging aspects I encountered during my experimentation was designing an action space that balanced granularity with computational feasibility. Through trial and error with different discretization strategies, I settled on a hierarchical action representation:
class HierarchicalEvacuationAction:
def __init__(self):
# Three-level hierarchy: Strategic → Tactical → Operational
self.action_levels = {
'strategic': {
'zone_prioritization': 'categorical', # Which zones to evacuate first
'resource_allocation': 'continuous', # How to distribute resources
'route_activation': 'binary' # Which routes to open/close
},
'tactical': {
'traffic_control': 'categorical', # Signal timing, lane reversals
'shelter_assignment': 'categorical', # Which shelters for which zones
'convoy_management': 'continuous' # Emergency vehicle routing
},
'operational': {
'individual_guidance': 'discrete', # Specific instructions to vehicles
'dynamic_rerouting': 'binary', # Real-time route updates
'communication_signals': 'categorical' # Public information updates
}
}
def encode_action(self, hierarchical_decision):
"""Encode hierarchical decision into flat action vector"""
encoded_parts = []
for level, decisions in hierarchical_decision.items():
for action_type, value in decisions.items():
encoding = self._encode_single_action(
action_type,
value,
level
)
encoded_parts.append(encoding)
return torch.cat(encoded_parts)
def decode_action(self, flat_vector):
"""Decode flat vector back to hierarchical structure"""
# Implement inverse of encoding with learned mappings
pointer = 0
decoded = {}
for level in ['strategic', 'tactical', 'operational']:
decoded[level] = {}
for action_type in self.action_levels[level]:
dim = self._get_action_dim(action_type, level)
slice = flat_vector[pointer:pointer+dim]
decoded[level][action_type] = self._decode_single_action(
action_type, slice, level
)
pointer += dim
return decoded
Challenges and Solutions: Lessons from the Trenches
Distributional Shift in Crisis Scenarios
While exploring offline RL for emergency scenarios, I encountered severe distributional shift problems. The training data (historical evacuations) didn't cover the extreme conditions seen in unprecedented wildfires. My solution was to implement a novel data augmentation strategy using generative models:
class CrisisScenarioAugmenter:
def __init__(self, base_dataset):
self.dataset = base_dataset
self.vae = self._train_scenario_vae()
def augment_training_data(self, n_samples=1000):
"""Generate plausible but novel crisis scenarios"""
augmented = []
for _ in range(n_samples):
# Sample from VAE latent space with controlled perturbations
z = torch.randn(1, 32)
# Apply crisis-specific transformations
z_crisis = self._apply_crisis_transformations(z)
# Decode to new scenario
scenario = self.vae.decode(z_crisis)
# Verify scenario plausibility
if self._verify_scenario_plausibility(scenario):
augmented.append(scenario)
return augmented
def _apply_crisis_transformations(self, latent_vector):
"""Apply transformations that simulate crisis conditions"""
# Amplify certain latent dimensions associated with crisis factors
crisis_dims = [2, 5, 8, 11] # Learned crisis indicators
transformed = latent_vector.clone()
for dim in crisis_dims:
# Increase variance in crisis dimensions
transformed[0, dim] *= 2.5
# Add correlation structure between crisis factors
if dim == 2: # Fire intensity
transformed[0, 5] += transformed[0, dim] * 0.3 # Wind correlation
transformed[0, 8] += transformed[0, dim] * 0.2 # Evacuation pressure
return transformed
Ethical Constraint Satisfaction
One of the most profound realizations from my research was that ethical constraints in evacuation planning cannot be reduced to simple penalty terms. Through studying ethical AI literature and consulting with emergency management professionals, I developed a multi-objective constraint satisfaction framework:
class EthicalConstraintSatisfaction:
def __init__(self):
self.constraints = {
'fairness': {
'type': 'group_parity',
'threshold': 0.85,
'groups': ['elderly', 'disabled', 'low_mobility']
},
'safety': {
'type': 'hard_constraint',
'metric': 'minimum_safety_margin',
'threshold': 0.95
},
'efficiency': {
'type': 'soft_constraint',
'metric': 'evacuation_time',
'weight': 0.3
}
}
def evaluate_decision(self, decision, state):
"""Evaluate decision against ethical constraints"""
scores = {}
violations = []
for constraint_name, spec in self.constraints.items():
if spec['type'] == 'hard_constraint':
score, violation = self._evaluate_hard_constraint(
decision, state, spec
)
if violation:
violations.append((constraint_name, violation))
else:
score = self._evaluate_soft_constraint(
decision, state, spec
)
scores[constraint_name] = score
return scores, violations
def _evaluate_hard_constraint(self, decision, state, spec):
"""Evaluate non-negotiable constraints"""
if spec['metric'] == 'minimum_safety_margin':
# Calculate minimum safety margin across all evacuees
margins = self._calculate_safety_margins(decision, state)
min_margin = torch.min(margins)
violation = min_margin < spec['threshold']
return min_margin.item(), violation
return None, False
Future Directions: Quantum-Enhanced Decision Making
My exploration of quantum computing applications for optimization problems led me to investigate quantum-enhanced versions of decision transformers. While still in early stages, I discovered promising approaches for handling the combinatorial explosion in evacuation routing:
python
class QuantumEnhancedRoutingOptimizer:
def __init__(self, n_qubits=16):
self.n_qubits = n_qubits
self.qaoa_circuit = self._build_qaoa_circuit()
def optimize_evacuation_routes(self, road_graph, population_nodes):
"""Use quantum approximate optimization for route planning"""
# Encode routing problem as QUBO
qubo_matrix = self._encode_routing_qubo(
road_graph,
population_nodes
)
# Prepare quantum circuit
quantum_state = self._prepare_quantum_state(qubo_matrix)
# Execute QAOA
optimized_routes = self._execute_qaoa(
quantum_state,
depth=4 # QAOA depth
)
return self._decode_quantum_solution(optimized_routes)
def _encode_routing_qubo(self, graph, population_nodes):
"""
Encode evacuation routing as Quadratic Unconstrained Binary Optimization
Each qubit represents a road segment being used/not used
"""
n_segments = len(graph.ed
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