Human-Aligned Decision Transformers for deep-sea exploration habitat design in hybrid quantum-classical pipelines

Human-Aligned Decision Transformers for deep-sea exploration habitat design in hybrid quantum-classical pipelines

Introduction: A Personal Dive into the Abyss

My journey into this niche began not in the deep sea, but in the equally complex depths of a reinforcement learning (RL) paper. I was experimenting with offline RL algorithms, trying to get a Decision Transformer to play Atari games from static datasets. While exploring the nuances of return-to-go conditioning, I had a sudden realization: what if the "return" we're conditioning on isn't a simple game score, but a multi-objective, human-defined preference for safety, efficiency, and sustainability? This thought experiment collided with a separate research thread I was pursuing on quantum annealing for combinatorial optimization. The fusion of these ideas—human-aligned sequential decision-making, transformer architectures, and quantum-enhanced optimization—formed the genesis of this exploration into designing habitats for one of humanity's final frontiers.

The challenge of deep-sea exploration habitat design is a perfect storm of constraints: immense pressure, limited energy, psychological isolation, and ecological sensitivity. Traditional optimization approaches often produce technically sound but humanly intolerable designs. Through studying recent advances in preference-based RL and inverse reinforcement learning, I learned that aligning AI systems with nuanced human values requires more than just reward shaping—it requires architectural integration of human feedback into the very fabric of decision-making. This article documents my research and experimentation in building a hybrid pipeline that leverages classical transformer models for sequential design reasoning, aligned with human preferences, and accelerated by quantum processors for solving the NP-hard optimization subproblems inherent in habitat configuration.

Technical Background: The Convergence of Three Frontiers

Decision Transformers and Human Alignment

Decision Transformers (DT) represent a paradigm shift in sequential decision-making. Unlike traditional RL that learns a policy through trial-and-error reward maximization, DT frames control as a sequence modeling problem. During my investigation of the original DT architecture, I found that its conditioning mechanism on desired returns (return-to-go) provides a natural interface for human preference injection. However, the standard implementation assumes a scalar reward signal.

One interesting finding from my experimentation with preference datasets was that human experts evaluating habitat designs don't provide scalar scores—they provide pairwise preferences, free-form critiques, and sometimes contradictory feedback across different value dimensions (safety vs. cost, privacy vs. social cohesion). This led me to explore the integration of Constitutional AI principles and Direct Preference Optimization (DPO) techniques directly into the transformer architecture.

Deep-Sea Habitat Design as a Sequential Decision Problem

Designing a habitat involves thousands of interdependent decisions: structural layout, life support system placement, material selection, emergency pathway planning, and psychological space allocation. Each decision affects subsequent options in complex ways. While learning about architectural optimization, I discovered that treating this as a single optimization problem leads to combinatorial explosion. However, framing it as a sequential decision process—where early decisions about pressure vessel geometry constrain later decisions about interior modularity—creates a tractable Markov Decision Process (MDP).

The state space includes environmental parameters (depth, temperature, currents), resource constraints (power, oxygen, waste processing capacity), and human factors (crew size, mission duration). The action space comprises design choices at various fidelity levels, from high-level architectural concepts down to specific equipment selections.

Quantum-Classical Hybrid Computing

Quantum computing, particularly Noisy Intermediate-Scale Quantum (NISQ) devices, excels at specific optimization problems but struggles with general sequential reasoning. My exploration of quantum annealing and variational quantum algorithms revealed their potential for solving the quadratic unconstrained binary optimization (QUBO) formulations that naturally arise in habitat design subproblems: equipment placement, pipe routing, and structural load distribution.

The hybrid approach I developed during my experimentation uses classical transformers for high-level reasoning and sequence generation, while offloading specific NP-hard subproblems to quantum processors. This division leverages the strengths of both paradigms.

Implementation Details: Building the Hybrid Pipeline

Architecture Overview

The system comprises three interconnected components:

1. Human-Aligned Decision Transformer: A modified transformer that ingests design state and human preference embeddings to generate design action sequences
2. Preference Learning Module: Converts human feedback into reward models and value embeddings
3. Quantum Optimization Layer: Solves specific combinatorial subproblems formulated as QUBOs

Core Decision Transformer with Human Preference Conditioning

During my experimentation with transformer architectures, I found that simply concatenating preference embeddings to the state wasn't sufficient for maintaining alignment throughout long design sequences. The solution was to implement a cross-attention mechanism between the design trajectory and a persistent preference context.

import torch
import torch.nn as nn
from transformers import GPT2Model, GPT2Config

class HumanAlignedDecisionTransformer(nn.Module):
    def __init__(self, state_dim, act_dim, hidden_size, max_length, num_preference_tokens):
        super().__init__()

        # State, action, and return embeddings
        self.state_embed = nn.Linear(state_dim, hidden_size)
        self.action_embed = nn.Linear(act_dim, hidden_size)
        self.return_embed = nn.Linear(1, hidden_size)

        # Preference embedding layer
        self.preference_embed = nn.Embedding(num_preference_tokens, hidden_size)

        # Modified GPT with cross-attention to preferences
        config = GPT2Config(
            n_embd=hidden_size,
            n_layer=6,
            n_head=8,
            n_positions=max_length * 3  # state, action, return
        )
        self.transformer = GPT2Model(config)

        # Cross-attention layers for preference conditioning
        self.preference_cross_attention = nn.MultiheadAttention(
            hidden_size, num_heads=4, batch_first=True
        )

        # Prediction heads
        self.state_pred = nn.Linear(hidden_size, state_dim)
        self.action_pred = nn.Linear(hidden_size, act_dim)

    def forward(self, states, actions, returns_to_go, preferences, timesteps):
        batch_size, seq_length = states.shape[0], states.shape[1]

        # Embed all inputs
        state_emb = self.state_embed(states)
        action_emb = self.action_embed(actions)
        return_emb = self.return_embed(returns_to_go.unsqueeze(-1))
        preference_emb = self.preference_embed(preferences).mean(dim=1, keepdim=True)

        # Stack embeddings along sequence dimension
        stacked_inputs = torch.stack(
            [state_emb, action_emb, return_emb], dim=1
        ).permute(0, 2, 1, 3).reshape(batch_size, 3 * seq_length, -1)

        # Add positional embeddings
        position_ids = timesteps.repeat(1, 3).reshape(batch_size, 3 * seq_length)
        position_embeddings = self.transformer.wpe(position_ids)
        transformer_inputs = stacked_inputs + position_embeddings

        # Transformer processing
        transformer_outputs = self.transformer(
            inputs_embeds=transformer_inputs,
            use_cache=False
        )
        hidden_states = transformer_outputs.last_hidden_state

        # Cross-attention with preferences
        attn_output, _ = self.preference_cross_attention(
            hidden_states,
            preference_emb.expand(-1, hidden_states.shape[1], -1),
            preference_emb.expand(-1, hidden_states.shape[1], -1)
        )
        hidden_states = hidden_states + attn_output  # Residual connection

        # Reshape back to get predictions
        hidden_states = hidden_states.reshape(batch_size, seq_length, 3, -1)

        # Predict next state and action
        state_preds = self.state_pred(hidden_states[:, :, 0, :])
        action_preds = self.action_pred(hidden_states[:, :, 1, :])

        return state_preds, action_preds

Preference Learning from Human Feedback

One of the most challenging aspects I encountered during my research was converting qualitative human feedback into quantitative reward signals. Through studying recent work on reinforcement learning from human feedback (RLHF), I implemented a two-stage approach:

class PreferenceRewardModel(nn.Module):
    """Learns to predict human preferences between design trajectories"""

    def __init__(self, state_dim, act_dim, hidden_size):
        super().__init__()

        # Trajectory encoder
        self.trajectory_encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=hidden_size,
                nhead=4,
                dim_feedforward=hidden_size * 4
            ),
            num_layers=3
        )

        # State-action embedding
        self.state_action_embed = nn.Sequential(
            nn.Linear(state_dim + act_dim, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, hidden_size)
        )

        # Reward head
        self.reward_head = nn.Sequential(
            nn.Linear(hidden_size, hidden_size // 2),
            nn.ReLU(),
            nn.Linear(hidden_size // 2, 1)
        )

    def forward(self, states, actions):
        # Embed each state-action pair
        batch_size, seq_len = states.shape[:2]
        state_action = torch.cat([states, actions], dim=-1)
        embeddings = self.state_action_embed(state_action)

        # Encode full trajectory
        trajectory_encoding = self.trajectory_encoder(embeddings)

        # Pool sequence dimension
        pooled = trajectory_encoding.mean(dim=1)

        # Predict reward
        rewards = self.reward_head(pooled)

        return rewards

def train_preference_model(model, preference_dataset, epochs=50):
    """Train using Bradley-Terry model for pairwise preferences"""

    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

    for epoch in range(epochs):
        total_loss = 0

        for batch in preference_dataset:
            states_A, actions_A, states_B, actions_B, preferences = batch

            # Get rewards for both trajectories
            rewards_A = model(states_A, actions_A)
            rewards_B = model(states_B, actions_B)

            # Bradley-Terry model loss
            logits = rewards_A - rewards_B
            loss = F.binary_cross_entropy_with_logits(
                logits, preferences.float()
            )

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            total_loss += loss.item()

        if epoch % 10 == 0:
            print(f"Epoch {epoch}, Loss: {total_loss/len(preference_dataset):.4f}")

    return model

Quantum-Classical Interface for Optimization Subproblems

The quantum layer addresses specific NP-hard subproblems. My experimentation with D-Wave's quantum annealer and IBM's quantum circuits led me to develop a flexible interface that can switch between quantum and classical solvers based on problem size and available hardware.

import dimod
from dwave.system import DWaveSampler, EmbeddingComposite
import numpy as np

class QuantumOptimizationLayer:
    """Solves habitat design subproblems using quantum annealing"""

    def __init__(self, use_quantum=True, quantum_solver='dwave'):
        self.use_quantum = use_quantum
        self.quantum_solver = quantum_solver

        if use_quantum and quantum_solver == 'dwave':
            # Initialize connection to quantum annealer
            self.sampler = EmbeddingComposite(DWaveSampler())
        else:
            # Fallback to classical simulated annealing
            self.sampler = dimod.SimulatedAnnealingSampler()

    def formulate_equipment_placement_qubo(self, habitat_layout, equipment_list):
        """
        Formulate equipment placement as QUBO
        Minimize: cable length + maintenance access + safety risks
        """
        num_locations = habitat_layout.num_cells
        num_equipment = len(equipment_list)

        # Binary variables: x_{i,j} = 1 if equipment j at location i
        num_variables = num_locations * num_equipment

        # Initialize QUBO matrix
        Q = np.zeros((num_variables, num_variables))

        # Add terms for cable length minimization
        for i in range(num_locations):
            for j in range(num_equipment):
                idx1 = i * num_equipment + j

                # Self-cost: installation difficulty
                Q[idx1, idx1] += habitat_layout.installation_cost[i, j]

                # Interaction costs: cable length between equipment
                for k in range(j + 1, num_equipment):
                    idx2 = i * num_equipment + k
                    cable_cost = equipment_list[j].cable_requirement * \
                                equipment_list[k].cable_requirement * \
                                habitat_layout.distance_matrix[i, i]
                    Q[idx1, idx2] += cable_cost

        # Constraint: each equipment placed exactly once
        for j in range(num_equipment):
            for i1 in range(num_locations):
                idx1 = i1 * num_equipment + j
                Q[idx1, idx1] -= 1000  # Encourages placement

                for i2 in range(i1 + 1, num_locations):
                    idx2 = i2 * num_equipment + j
                    Q[idx1, idx2] += 2000  # Penalizes multiple placements

        return Q

    def solve_qubo(self, Q, num_reads=1000):
        """Solve QUBO using quantum or classical annealing"""

        # Convert to BQM
        bqm = dimod.BinaryQuadraticModel.from_numpy_matrix(
            Q,
            offset=0.0,
            vartype=dimod.BINARY
        )

        # Sample solutions
        sampleset = self.sampler.sample(
            bqm,
            num_reads=num_reads,
            annealing_time=100  # microseconds for quantum
        )

        # Get best solution
        best_solution = sampleset.first.sample
        best_energy = sampleset.first.energy

        return best_solution, best_energy, sampleset

Integrated Training Pipeline

The complete training pipeline alternates between preference learning, decision transformer training, and quantum-optimized action refinement:

class HybridTrainingPipeline:
    """Orchestrates training of the complete system"""

    def __init__(self, config):
        self.config = config

        # Initialize models
        self.dt_model = HumanAlignedDecisionTransformer(
            state_dim=config.state_dim,
            act_dim=config.act_dim,
            hidden_size=config.hidden_size,
            max_length=config.max_length,
            num_preference_tokens=config.num_preference_tokens
        )

        self.reward_model = PreferenceRewardModel(
            state_dim=config.state_dim,
            act_dim=config.act_dim,
            hidden_size=config.hidden_size
        )

        self.quantum_layer = QuantumOptimizationLayer(
            use_quantum=config.use_quantum,
            quantum_solver=config.quantum_solver
        )

    def train_iteration(self, dataset, human_feedback_batch):
        """Single training iteration"""

        # Phase 1: Update reward model from human feedback
        self.reward_model = train_preference_model(
            self.reward_model,
            human_feedback_batch,
            epochs=self.config.reward_train_epochs
        )

        # Phase 2: Generate synthetic returns using reward model
        with torch.no_grad():
            synthetic_returns = []
            for states, actions in dataset:
                returns = self.reward_model(states, actions)
                synthetic_returns.append(returns)

        # Phase 3: Train Decision Transformer with synthetic returns
        dt_optimizer = torch.optim.Adam(
            self.dt_model.parameters(),
            lr=self.config.dt_learning_rate
        )

        for epoch in range(self.config.dt_train_epochs):
            for states, actions, _, preferences, timesteps in dataset:
                # Get synthetic returns for this batch
                returns = self.reward_model(states, actions)

                # Predict next states and actions
                state_preds, action_preds = self.dt_model(
                    states[:, :-1], actions[:, :-1],
                    returns[:, :-1], preferences, timesteps[:, :-1]
                )

                # Calculate losses
                state_loss = F.mse_loss(state_preds, states[:, 1:])
                action_loss = F.mse_loss(action_preds, actions[:, 1:])

                total_loss = state_loss + action_loss

                dt_optimizer.zero_grad()
                total_loss.backward()
                dt_optimizer.step()

        # Phase 4: Quantum refinement of specific design decisions
        if self.config.use_quantum_refinement:
            self.quantum_refinement_step(dataset)

    def quantum_refinement_step(self, dataset):
        """Use quantum optimization to refine specific design decisions"""

        for batch in dataset:
            states, actions, _, _, _ = batch

            # Extract equipment placement subproblem
            equipment_placement_state = self.extract_equipment_subproblem(states)

            # Formulate as QUBO
            Q = self.quantum_layer.formulate_equipment_placement_qubo(
                equipment_placement_state,
                self.config.equipment_list
            )

            # Solve with quantum/classical annealing
            solution, energy, _ = self.quantum_layer.solve_qubo(Q)

            # Incorporate solution back into actions
            refined_actions = self.incorporate_quantum_solution(
                actions, solution, equipment_placement_state
            )

            # Update dataset with refined actions
            batch[1] = refined_actions

Real-World Applications: From Simulation to Abyssal Deployment

Case Study: Hadal Zone Research Station

During my research, I simulated the design of a research station for the hadal zone (6,000-11,