GON: Generalizable Outcome Networks for Learning Action Effects in Physical Systems

Pretrained control models such as Decision Transformer, Gato, RT-1/RT-2, and OpenVLA learn policies from large collections of observed trajectories to generalize across tasks, environments, and operating conditions. However, when control actions take time to affect a physical system, external conditions can change the system during the same period. We study this problem in smart buildings, where indoor temperature is affected by both heating, ventilation, and air conditioning (HVAC) control and external conditions such as weather, sunlight, and occupancy. A recorded temperature change alone does not show how much was caused by HVAC control. As a result, a controller trained on recorded trajectories may not reliably learn what its actions do, leading to higher energy use or reduced comfort when conditions change. To address this problem, we design GON (Generalizable Outcome Networks), which learns how sequences of control actions affect a physical system over time. During training, GON simulates different HVAC action sequences from the same building state, keeping external conditions the same across the simulations. Comparing the resulting temperature trajectories shows how the building's response changes when the action sequence changes. An outcome model uses recent building history and a proposed HVAC action sequence to predict temperature and energy use. Given a target temperature trajectory, GON uses conditional flow matching to generate several candidate HVAC action sequences. At deployment, GON scores each sequence based on predicted discomfort and energy use, applies its first action, and repeats the process after observing the building. We evaluate GON on 100 unseen buildings using a single frozen controller without target-building adaptation. Compared with Decision Pretrained Transformer, GON reduces energy use by 34% and comfort penalty by 43% on the comfort-focused task. On the energy-focused task, it reduces energy use by 18% and comfort penalty by 43%. GON performs comparably to model predictive control (MPC) configured separately for each building.