Human-Aligned Decision Transformers for planetary geology survey missions with zero-trust governance guarantees

Introduction: The Martian Conundrum

It was 3 AM, and I was staring at a simulation of the Jezero Crater on Mars. My reinforcement learning agent, trained on thousands of hours of simulated geological survey data, had just made a decision that was technically optimal but fundamentally wrong. The agent had identified a scientifically promising rock formation but chose to sacrifice its spectrometer calibration to reach it faster—a decision no human geologist would ever make. This moment crystallized a fundamental challenge I’d been exploring for months: how do we create AI systems that make decisions aligned with human values, especially when those systems operate in environments where trust cannot be assumed?

My journey into human-aligned AI began with studying decision transformers, a fascinating architecture that frames reinforcement learning as a sequence modeling problem. While exploring offline RL algorithms, I discovered that traditional approaches often optimize for reward maximization without understanding the underlying human intent. This realization led me down a path of researching how to embed human values directly into decision-making architectures, particularly for high-stakes applications like planetary exploration where communication delays and environmental uncertainties make real-time human oversight impossible.

Technical Background: Decision Transformers Reimagined

The Foundation: From Reward Maximization to Trajectory Modeling

Traditional reinforcement learning approaches, as I learned through extensive experimentation, treat decision-making as a Markov Decision Process where an agent learns a policy π(a|s) to maximize cumulative reward. Decision transformers, introduced by Chen et al. in 2021, revolutionized this paradigm by treating RL as a sequence modeling problem. The key insight I discovered while implementing these systems is that by conditioning on desired returns (rewards-to-go), we can generate trajectories that achieve specific performance levels.

import torch
import torch.nn as nn
import numpy as np

class DecisionTransformerBlock(nn.Module):
"""Core transformer block for decision sequence modeling"""
def __init__(self, hidden_dim=128, num_heads=8):
super().__init__()
self.attention = nn.MultiheadAttention(hidden_dim, num_heads)
self.norm1 = nn.LayerNorm(hidden_dim)
self.norm2 = nn.LayerNorm(hidden_dim)
self.mlp = nn.Sequential(
nn.Linear(hidden_dim, 4*hidden_dim),
nn.GELU(),
nn.Linear(4*hidden_dim, hidden_dim)
)

def forward(self, x, attn_mask=None):
# Self-attention with residual connection
attn_out, _ = self.attention(x, x, x, attn_mask=attn_mask)
x = self.norm1(x + attn_out)

# Feed-forward with residual
mlp_out = self.mlp(x)
x = self.norm2(x + mlp_out)
return x

During my research into transformer architectures for decision-making, I found that the sequence formulation naturally accommodates multiple modalities—states, actions, and returns—all processed through the same attention mechanism. This unified representation became crucial when I started exploring how to incorporate human preferences and safety constraints.

The Alignment Problem: Beyond Reward Functions

One of the most significant insights from my experimentation was that reward functions are insufficient for capturing human values. While studying inverse reinforcement learning papers, I realized that human preferences are often implicit, contextual, and sometimes contradictory. My breakthrough came when I started treating alignment as a multi-objective optimization problem where human values serve as constraints rather than objectives.

class HumanAlignedDecisionTransformer(nn.Module):
"""Decision transformer with explicit human value alignment"""
def __init__(self, state_dim, action_dim, hidden_dim=256):
super().__init__()

# Embedding layers for different modalities
self.state_embed = nn.Linear(state_dim, hidden_dim)
self.action_embed = nn.Linear(action_dim, hidden_dim)
self.return_embed = nn.Linear(1, hidden_dim)
self.value_embed = nn.Linear(3, hidden_dim)  # Human values: safety, science, efficiency

# Transformer backbone
self.transformer = nn.TransformerEncoder(
nn.TransformerEncoderLayer(hidden_dim, nhead=8),
num_layers=6
)

# Output heads
self.action_head = nn.Linear(hidden_dim, action_dim)
self.value_head = nn.Linear(hidden_dim, 3)  # Predict value compliance

def forward(self, states, actions, returns_to_go, human_values):
# Embed all modalities
state_emb = self.state_embed(states)
action_emb = self.action_embed(actions)
return_emb = self.return_embed(returns_to_go.unsqueeze(-1))
value_emb = self.value_embed(human_values)

# Concatenate embeddings with positional encoding
seq_len = states.shape[1]
positions = torch.arange(seq_len).unsqueeze(0).unsqueeze(-1)
pos_emb = self.position_embed(positions.float())

# Combine embeddings (simplified for clarity)
combined = state_emb + action_emb + return_emb + value_emb + pos_emb

# Process through transformer
transformer_out = self.transformer(combined)

# Predict next action and value compliance
next_action = self.action_head(transformer_out[:, -1, :])
value_compliance = self.value_head(transformer_out[:, -1, :])

return next_action, value_compliance

Implementation Details: Zero-Trust Governance Architecture

The Core Innovation: Verifiable Decision Traces

Through my exploration of blockchain and cryptographic verification systems, I developed a zero-trust governance framework that doesn’t rely on trusting the AI system itself. Instead, it verifies that every decision complies with predefined human values. The key insight from this research was that we could use cryptographic commitments to create immutable decision logs that can be audited post-facto.

import hashlib
import json
from typing import Dict, Any
from dataclasses import dataclass, asdict
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.asymmetric import ec

@dataclass
class DecisionRecord:
"""Immutable record of a decision with cryptographic verification"""
timestamp: float
state: np.ndarray
action: np.ndarray
predicted_values: np.ndarray
human_values: np.ndarray
compliance_score: float
previous_hash: str

def to_hash(self) -> str:
"""Create cryptographic hash of decision record"""
data_str = json.dumps({
'timestamp': self.timestamp,
'state': self.state.tolist(),
'action': self.action.tolist(),
'predicted_values': self.predicted_values.tolist(),
'human_values': self.human_values.tolist(),
'compliance_score': self.compliance_score,
'previous_hash': self.previous_hash
}, sort_keys=True)

return hashlib.sha256(data_str.encode()).hexdigest()

class ZeroTrustGovernance:
"""Zero-trust governance layer for decision verification"""

def __init__(self, public_key: ec.EllipticCurvePublicKey):
self.public_key = public_key
self.decision_chain = []
self.compliance_threshold = 0.85

def verify_decision(self, decision: DecisionRecord) -> bool:
"""Verify decision complies with human values"""

# 1. Check cryptographic chain integrity
if self.decision_chain:
last_record = self.decision_chain[-1]
if decision.previous_hash != last_record.to_hash():
return False

# 2. Verify value compliance
value_differences = np.abs(decision.predicted_values - decision.human_values)
max_deviation = np.max(value_differences)

# 3. Check against safety boundaries
safety_violated = any(v < 0.1 for v in decision.predicted_values[:2])  # Safety & science

return (decision.compliance_score >= self.compliance_threshold and
max_deviation < 0.3 and not safety_violated)

def add_decision(self, decision: DecisionRecord) -> bool:
"""Add decision to chain if it passes verification"""
if self.verify_decision(decision):
self.decision_chain.append(decision)

# Create cryptographic signature
decision_hash = decision.to_hash().encode()
# Signature logic would go here

return True
return False

During my experimentation with this architecture, I discovered that the cryptographic overhead was minimal compared to the transformer computations, making it practical for real-time systems. The real challenge, as I learned through trial and error, was designing value representations that were both expressive enough to capture human intent and compact enough for efficient verification.

Multi-Modal Value Embeddings

One interesting finding from my experimentation with planetary geology missions was that human values need to be represented across multiple modalities. A geologist’s decision-making incorporates visual patterns, spectral data, temporal sequences, and spatial relationships. My solution was to create a multi-modal value embedding space:

class MultiModalValueEncoder(nn.Module):
"""Encode human values from multiple modalities"""

def __init__(self,
image_dim: int = 512,
spectral_dim: int = 256,
spatial_dim: int = 128):
super().__init__()

# Modality-specific encoders
self.image_encoder = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, stride=2),
nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=3, stride=2),
nn.ReLU(),
nn.AdaptiveAvgPool2d((4, 4)),
nn.Flatten(),
nn.Linear(64*4*4, image_dim)
)

self.spectral_encoder = nn.Sequential(
nn.Linear(spectral_dim, 128),
nn.ReLU(),
nn.Linear(128, 256),
nn.ReLU(),
nn.Linear(256, image_dim)
)

# Cross-modal attention
self.cross_attention = nn.MultiheadAttention(image_dim, num_heads=4)

# Value projection
self.value_projection = nn.Linear(image_dim * 3, 3)  # Safety, science, efficiency

def forward(self, image, spectral_data, spatial_context):
# Encode each modality
img_features = self.image_encoder(image)
spec_features = self.spectral_encoder(spectral_data)

# Cross-modal attention
combined = torch.stack([img_features, spec_features, spatial_context], dim=1)
attended, _ = self.cross_attention(combined, combined, combined)

# Project to human values
flattened = attended.flatten(start_dim=1)
human_values = torch.sigmoid(self.value_projection(flattened))

return human_values

Through studying cognitive science papers on expert decision-making, I realized that human geologists don’t consciously separate these modalities—they form a unified gestalt. My architecture attempts to mimic this by using cross-modal attention to create integrated value representations.

Real-World Applications: Planetary Geology Survey Missions

Autonomous Rock Sampling with Value Alignment

During my simulation experiments, I implemented a complete planetary geology survey system that demonstrated the practical application of human-aligned decision transformers. The system needed to balance multiple competing objectives: scientific value (sample quality), mission safety (rover integrity), and operational efficiency (power consumption).

class PlanetaryGeologyAgent:
"""Complete agent for autonomous planetary geology surveys"""

def __init__(self, config: Dict[str, Any]):
self.decision_transformer = HumanAlignedDecisionTransformer(
state_dim=config['state_dim'],
action_dim=config['action_dim']
)

self.value_encoder = MultiModalValueEncoder()
self.governance = ZeroTrustGovernance(config['public_key'])

# Human value profiles for different mission phases
self.value_profiles = {
'exploration': [0.2, 0.7, 0.1],    # Emphasize science
'sampling': [0.4, 0.5, 0.1],       # Balance safety and science
'transit': [0.6, 0.1, 0.3],        # Emphasize safety and efficiency
'emergency': [0.8, 0.1, 0.1]       # Maximum safety
}

def decide_next_action(self,
sensor_data: Dict[str, np.ndarray],
mission_phase: str) -> Dict[str, Any]:
"""Make a human-aligned decision for planetary survey"""

# Encode current state
state = self.encode_state(sensor_data)

# Get human values for current phase
human_values = torch.tensor(
self.value_profiles[mission_phase],
dtype=torch.float32
)

# Generate decision using transformer
with torch.no_grad():
action, predicted_values = self.decision_transformer(
state.unsqueeze(0),
torch.zeros(1, 1, self.decision_transformer.action_dim),  # Placeholder
torch.tensor([[100.0]]),  # Target return
human_values.unsqueeze(0)
)

# Calculate compliance score
compliance = 1.0 - torch.mean(torch.abs(predicted_values - human_values))

# Create verifiable decision record
decision_record = DecisionRecord(
timestamp=time.time(),
state=state.numpy(),
action=action.squeeze().numpy(),
predicted_values=predicted_values.squeeze().numpy(),
human_values=human_values.numpy(),
compliance_score=compliance.item(),
previous_hash=self.governance.decision_chain[-1].to_hash()
if self.governance.decision_chain else "genesis"
)

# Verify and record decision
if self.governance.add_decision(decision_record):
return {
'action': action.squeeze().numpy(),
'compliance': compliance.item(),
'verified': True
}
else:
# Fallback to safe action
return self.get_safe_fallback_action()

One of the most valuable lessons from implementing this system was that the human value profiles needed to be context-dependent. Through experimentation with different geological scenarios, I found that static value weights were insufficient—the system needed to dynamically adjust its value priorities based on environmental conditions and mission progress.

Adaptive Value Learning from Human Feedback

My research revealed a crucial insight: human alignment isn’t a one-time calibration but an ongoing process. I developed a system that could learn from sparse human feedback during mission operations:

class AdaptiveValueLearner:
"""Learn and adapt human value representations from feedback"""

def __init__(self, initial_values: np.ndarray, learning_rate: float = 0.01):
self.current_values = torch.tensor(initial_values, requires_grad=True)
self.optimizer = torch.optim.Adam([self.current_values], lr=learning_rate)
self.feedback_buffer = []

def incorporate_feedback(self,
decision_record: DecisionRecord,
human_feedback: np.ndarray,
feedback_confidence: float):
"""Incorporate human feedback to refine value representations"""

# Store feedback for batch learning
self.feedback_buffer.append({
'predicted': decision_record.predicted_values,
'feedback': human_feedback,
'confidence': feedback_confidence
})

# Learn from accumulated feedback
if len(self.feedback_buffer) >= 10:
self.update_from_feedback_batch()

def update_from_feedback_batch(self):
"""Batch update of value representations"""
losses = []

for feedback in self.feedback_buffer:
# Calculate loss weighted by confidence
loss = torch.mean(
(self.current_values - torch.tensor(feedback['feedback'])) ** 2
) * feedback['confidence']
losses.append(loss)

# Optimize value representation
total_loss = torch.stack(losses).mean()
self.optimizer.zero_grad()
total_loss.backward()
self.optimizer.step()

# Clamp values to valid range
with torch.no_grad():
self.current_values.clamp_(0.0, 1.0)

# Clear buffer
self.feedback_buffer = []

return total_loss.item()

During my testing, I discovered that this adaptive approach was particularly valuable for handling novel situations that weren’t covered in training. The system could gradually align its value representations with human expectations even in unfamiliar geological contexts.

Challenges and Solutions

The Curse of Sparse Rewards in Planetary Exploration

One of the most significant challenges I encountered during my experimentation was the extreme sparsity of reward signals in planetary exploration. Traditional RL algorithms struggle when meaningful feedback might occur only once per day (or less). My solution involved developing a hierarchical value decomposition approach:

python
class HierarchicalValueDecomposition:
"""Decompose sparse mission-level values into dense sub-values"""

def __init__(self, num_levels: int = 3):
self.value_hierarchy = {
'mission': ['science_return', 'safety_record', 'efficiency'],
'daily': ['sample_quality', 'instrument_health', 'power_balance'],
'hourly': ['traversal_safety', 'data_quality', 'energy_use']
}

self.value_mappings = self.learn_value_mappings()

def learn_value_mappings(self) -> Dict[str, nn.Module]:
"""Learn mappings between hierarchical value levels"""
mappings = {}

for higher_level, lower_level in zip(
list(self.value_hierarchy.keys())[:-1],
list(self.value_hierarchy.keys())[1:]
):
higher_dim = len(self