Human-Aligned Decision Transformers for smart agriculture microgrid orchestration with inverse simulation verification
Introduction: The Learning Journey from Grids to Agents
My journey into this intersection of AI and sustainable agriculture began not in a lab, but in a field. A few years ago, while consulting on a precision agriculture project, I witnessed a critical failure. A smart irrigation system, powered by a solar microgrid, had misinterpreted a weather forecast and drained its battery reserves overnight, leaving sensors and pumps dead during the morning's crucial watering window. The system was "optimal" according to its cost-minimization algorithm, but it had catastrophically failed to align with the farmer's fundamental priority: crop survival.
This experience sparked a multi-year research obsession. I began exploring how we could build AI controllers that don't just optimize for abstract metrics like "efficiency" or "cost," but truly understand and align with human-defined priorities, even when those priorities are complex, multi-faceted, and sometimes contradictory. Through studying reinforcement learning, offline RL, and the emerging field of sequence modeling for decision-making, I discovered the potential of Decision Transformers. However, I quickly realized a fundamental gap: these models could learn patterns from data, but they lacked a robust mechanism for verifying that their decisions would lead to outcomes humans actually wanted in novel, out-of-distribution situations—like a sudden, unpredicted frost or a critical equipment failure.
My exploration led me to combine two powerful concepts: Human-Aligned Decision Transformers trained on expert demonstrations and preferences, and Inverse Simulation Verification, a novel paradigm where every proposed action sequence is "run forward" in a simulated digital twin to predict its outcome, which is then evaluated by an inverse model that answers: "Does this outcome match a human-desirable state?" This article details the technical architecture, implementation challenges, and profound insights I gained from building and testing this system for smart agriculture microgrid orchestration.
Technical Background: Bridging Decision Transformers, Preference Learning, and Simulation
The Core Problem: Microgrid Orchestration as a Sequential Decision Problem
A smart agriculture microgrid integrates renewable sources (solar PV, small wind), energy storage (batteries), controllable loads (irrigation pumps, greenhouse HVAC, processing equipment), and connection to the main grid. The orchestration agent must decide, at each time step (e.g., every 15 minutes), how much energy to draw from or inject into the grid, how to charge/discharge the battery, and which loads to prioritize or shed. The objective is multi-objective: minimize electricity cost, maximize renewable self-consumption, ensure task completion (e.g., irrigate field X by 10 AM), and maintain grid stability, all while operating within physical constraints (battery SOC limits, inverter capacities).
Traditional approaches use Model Predictive Control (MPC) with mixed-integer linear programming. However, as I learned through implementing several MPC controllers, they struggle with the non-linear, uncertain, and high-dimensional nature of real farm operations. The "cost function" is notoriously difficult to specify to capture all human preferences.
Decision Transformers: Offline RL as Sequence Modeling
The breakthrough for me came from the 2021 paper "Decision Transformer: Reinforcement Learning via Sequence Modeling." The core idea is elegant: treat reinforcement learning as a conditional sequence modeling problem. Instead of learning a value function or policy gradient, you model the probability of the optimal action given past states, actions, and a desired return-to-go (RTG).
In my experimentation, I adapted this for a continuous action space. The model architecture is an autoregressive transformer that takes a trajectory sequence as input:
τ = (R̂₁, s₁, a₁, R̂₂, s₂, a₂, ..., R̂_T, s_T, a_T)
Where R̂_t is the return-to-go at time t, s_t is the state, and a_t is the action. The model is trained via supervised learning on trajectories collected from an expert (e.g., a human operator or a near-optimal planner), learning to predict a_t given the previous sequence and the desired RTG.
import torch
import torch.nn as nn
import numpy as np
class DecisionTransformerBlock(nn.Module):
"""A single transformer block for the Decision Transformer."""
def __init__(self, hidden_dim, num_heads, dropout_rate=0.1):
super().__init__()
self.attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout=dropout_rate, batch_first=True)
self.mlp = nn.Sequential(
nn.Linear(hidden_dim, 4 * hidden_dim),
nn.GELU(),
nn.Linear(4 * hidden_dim, hidden_dim),
nn.Dropout(dropout_rate)
)
self.ln1 = nn.LayerNorm(hidden_dim)
self.ln2 = nn.LayerNorm(hidden_dim)
self.dropout = nn.Dropout(dropout_rate)
def forward(self, x, attn_mask=None):
# Self-attention with residual
attn_out, _ = self.attention(x, x, x, attn_mask=attn_mask)
x = x + self.dropout(attn_out)
x = self.ln1(x)
# Feed-forward with residual
mlp_out = self.mlp(x)
x = x + self.dropout(mlp_out)
x = self.ln2(x)
return x
class DecisionTransformer(nn.Module):
"""Decision Transformer for continuous action prediction in microgrid control."""
def __init__(self, state_dim, act_dim, hidden_dim=128, num_layers=3, num_heads=4, max_len=100):
super().__init__()
self.state_embed = nn.Linear(state_dim, hidden_dim)
self.act_embed = nn.Linear(act_dim, hidden_dim)
self.rtg_embed = nn.Linear(1, hidden_dim)
# Learned positional embeddings
self.pos_embed = nn.Parameter(torch.zeros(1, max_len, hidden_dim))
# Transformer blocks
self.blocks = nn.ModuleList([
DecisionTransformerBlock(hidden_dim, num_heads) for _ in range(num_layers)
])
# Prediction heads
self.act_head = nn.Linear(hidden_dim, act_dim)
self.ln = nn.LayerNorm(hidden_dim)
def forward(self, states, actions, rtgs, timesteps, attention_mask=None):
batch_size, seq_len = states.shape[0], states.shape[1]
# Embeddings
state_emb = self.state_embed(states)
act_emb = self.act_embed(actions)
rtg_emb = self.rtg_embed(rtgs.unsqueeze(-1))
# Sequence ordering: [rtg, state, action] for each timestep
token_embeddings = torch.stack((rtg_emb, state_emb, act_emb), dim=2)
token_embeddings = token_embeddings.reshape(batch_size, 3*seq_len, -1)
# Add positional embeddings
token_embeddings = token_embeddings + self.pos_embed[:, :3*seq_len, :]
# Apply transformer blocks
x = token_embeddings
for block in self.blocks:
x = block(x, attn_mask=attention_mask)
x = self.ln(x)
# Predict next action (extract predictions for action tokens)
# We predict action for the last timestep
x = x[:, -3:, :] # Last three tokens: [rtg_T, state_T, action_T]
act_pred = self.act_head(x[:, 2, :]) # Prediction from action token position
return act_pred
Human Alignment via Preference Modeling and Inverse Reward Design
Training a Decision Transformer on expert data is a start, but what if the expert data is suboptimal or doesn't cover all scenarios? Through my research into inverse reinforcement learning (IRL) and preference-based RL, I realized alignment requires learning the underlying reward function that explains human decisions and preferences.
I implemented a preference model that learns from pairwise comparisons of trajectory segments. Given two segments (τi, τ_j), a human labeler indicates which is better. The model learns a reward function `rθ(s, a)` such that the cumulative reward of the preferred segment is higher.
class PreferenceRewardModel(nn.Module):
"""Neural network to learn a reward function from human preferences."""
def __init__(self, state_dim, act_dim, hidden_dim=64):
super().__init__()
self.net = nn.Sequential(
nn.Linear(state_dim + act_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 1) # Scalar reward
)
def forward(self, states, actions):
x = torch.cat([states, actions], dim=-1)
return self.net(x)
def learn_from_preferences(self, segment_pairs, preferences, optimizer, epochs=100):
"""Train reward model on pairwise preferences."""
losses = []
for epoch in range(epochs):
total_loss = 0
for (seg1, seg2), pref in zip(segment_pairs, preferences):
# seg: (states, actions) tuple
s1, a1 = seg1
s2, a2 = seg2
# Compute cumulative rewards for each segment
r1 = self(s1, a1).sum()
r2 = self(s2, a2).sum()
# Bradley-Terry model for preference probability
logits = torch.stack([r1, r2], dim=0)
pref_probs = torch.softmax(logits, dim=0)
# Cross-entropy loss
target = torch.tensor([1.0, 0.0] if pref == 0 else [0.0, 1.0])
loss = -torch.log(pref_probs).dot(target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
avg_loss = total_loss / len(segment_pairs)
losses.append(avg_loss)
if epoch % 20 == 0:
print(f"Epoch {epoch}, Loss: {avg_loss:.4f}")
return losses
The key insight from my experimentation was that this learned reward function could then be used to relabel the returns-to-go (RTGs) in the expert dataset. Instead of using the original RTG (based on, say, electricity cost), we compute a new "human-aligned RTG" using the learned reward model, effectively retrofitting the expert demonstrations with a reward signal that better captures human preferences.
Implementation: The Integrated Architecture
The complete system I developed integrates three core components:
- Human-Aligned Decision Transformer (HADT): A DT trained on preference-relabeled demonstrations.
- Digital Twin Simulator: A physics-informed model of the microgrid and farm operations.
- Inverse Verification Model (IVM): A classifier that maps predicted future states to a "human-desirability" score.
System Architecture and Training Pipeline
class HumanAlignedMicrogridOrchestrator:
"""Complete system for human-aligned microgrid control with inverse verification."""
def __init__(self, state_dim, act_dim, config):
self.state_dim = state_dim
self.act_dim = act_dim
# Core models
self.decision_transformer = DecisionTransformer(state_dim, act_dim,
hidden_dim=config['dt_hidden_dim'])
self.reward_model = PreferenceRewardModel(state_dim, act_dim,
hidden_dim=config['rm_hidden_dim'])
self.inverse_verifier = InverseVerificationModel(state_dim,
hidden_dim=config['ivm_hidden_dim'])
# Digital twin simulator (simplified interface)
self.simulator = MicrogridDigitalTwin(config['sim_params'])
# Training buffers
self.demo_buffer = [] # Expert demonstrations
self.pref_buffer = [] # Preference comparisons
def train_pipeline(self, expert_demos, human_preferences, num_epochs=1000):
"""
Three-phase training pipeline:
1. Train reward model on human preferences
2. Relabel expert demos with human-aligned rewards
3. Train Decision Transformer on relabeled demos
"""
print("Phase 1: Learning reward function from human preferences...")
# Train reward model
rm_optimizer = torch.optim.Adam(self.reward_model.parameters(), lr=1e-3)
self.reward_model.learn_from_preferences(human_preferences, rm_optimizer, epochs=200)
print("Phase 2: Relabeling demonstrations with human-aligned rewards...")
# Relabel demonstrations
relabeled_demos = []
for states, actions, _ in expert_demos:
# Compute human-aligned rewards for each step
with torch.no_grad():
rewards = self.reward_model(states, actions)
# Compute new return-to-go from the end
rtgs = torch.flip(torch.cumsum(torch.flip(rewards, [0]), 0), [0])
relabeled_demos.append((states, actions, rtgs))
print("Phase 3: Training Human-Aligned Decision Transformer...")
# Train DT on relabeled data
dt_optimizer = torch.optim.AdamW(self.decision_transformer.parameters(), lr=6e-4)
self._train_decision_transformer(relabeled_demos, dt_optimizer, num_epochs)
print("Phase 4: Training Inverse Verification Model...")
# Train IVM on examples of desirable vs undesirable outcomes
self._train_inverse_verifier(expert_demos)
def plan_with_verification(self, current_state, target_rtg, horizon=24, num_candidates=10):
"""
Generate action plans with inverse simulation verification.
Returns the plan with highest verified desirability.
"""
candidate_plans = []
# Generate multiple candidate plans (could use beam search or sampling)
for _ in range(num_candidates):
plan = self._generate_plan(current_state, target_rtg, horizon)
# Inverse simulation: run plan through digital twin
simulated_states = self.simulator.rollout(current_state, plan)
# Verify desirability of outcome
with torch.no_grad():
desirability_score = self.inverse_verifier(simulated_states[-1].unsqueeze(0))
candidate_plans.append({
'plan': plan,
'simulated_states': simulated_states,
'desirability': desirability_score.item()
})
# Select plan with highest verified desirability
best_plan = max(candidate_plans, key=lambda x: x['desirability'])
# Safety check: if desirability is below threshold, fall back to safe heuristic
if best_plan['desirability'] < 0.5:
print("Warning: No sufficiently desirable plan found. Using safe fallback.")
return self._safe_fallback_plan(current_state)
return best_plan['plan']
Inverse Simulation Verification: The Safety Net
The most innovative component, born from my frustration with "black box" AI decisions, is the Inverse Verification Model. While the HADT generates plans aimed at achieving high human-aligned returns, the IVM answers: "If we execute this plan, will the resulting system state be one that humans would consider good?"
I trained the IVM as a binary classifier on examples of "desirable" and "undesirable" terminal states. Desirable states were extracted from successful expert trajectories (e.g., crops watered, battery sufficiently charged, costs low). Undesirable states were generated by perturbing good states or sampling from failed control episodes.
class InverseVerificationModel(nn.Module):
"""Classifies whether a predicted future state is human-desirable."""
def __init__(self, state_dim, hidden_dim=64):
super().__init__()
self.net = nn.Sequential(
nn.Linear(state_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 1),
nn.Sigmoid() # Output probability of being desirable
)
def forward(self, state):
return self.net(state)
def train_verifier(self, desirable_states, undesirable_states, epochs=100):
"""Train to distinguish desirable vs undesirable outcomes."""
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
criterion = nn.BCELoss()
# Create labeled dataset
states = torch.cat([desirable_states, undesirable_states], dim=0)
labels = torch.cat([
torch.ones(len(desirable_states), 1),
torch.zeros(len(undesirable_states), 1)
], dim=0)
for epoch in range(epochs):
optimizer.zero_grad()
predictions = self(states)
loss = criterion(predictions, labels)
loss.backward()
optimizer.step()
if epoch % 20 == 0:
accuracy = ((predictions > 0.5) == (labels > 0.5)).float().mean()
print(f"Epoch {epoch}, Loss: {loss.item():.4f}, Accuracy: {accuracy:.4f}")
During deployment, every plan generated by the HADT is "pre-executed" in the digital twin simulator. The resulting predicted terminal state is fed to the IVM. If the desirability score
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