NASA’s new dark energy space telescope can also detect killer asteroids
technologyreview.com·2h ago
AI Research
arXiv CS.AI·May 28·6 min read
PIRS: Physics-Informed Reward Shaping for SAC-Based Building Energy Management
TL;DR
Building energy management systems struggle to balance energy efficiency with occupant comfort using standard reinforcement learning. PIRS (Physics-Informed Reward Shaping) embeds domain knowledge into SAC (Soft Actor-Critic), a deep reinforcement learning algorithm, to guide control decisions.
✦ Why It Matters
Engineers can embed domain physics into RL agents to accelerate learning and improve real-world building control performance.
Key Takeaways
- Embed physics constraints into reward functions to guide RL agents toward realistic solutions.
- SAC algorithm balances exploration-exploitation better than standard Q-learning for continuous control.
- Physics-informed approaches reduce training time and improve convergence in building automation.
- Combine energy efficiency metrics with comfort constraints in reward design for practical deployment.
Full Summary
Building HVAC (heating, ventilation, air conditioning) control is a complex optimization problem: reducing energy consumption often conflicts with maintaining comfortable indoor temperatures. Standard reinforcement learning agents learn control policies through trial-and-error without leveraging known physics of thermal dynamics.
PIRS integrates physics-based constraints directly into the reward function of SAC, a state-of-the-art deep reinforcement learning algorithm that balances exploration and exploitation. This physics-informed reward shaping guides the agent toward physically plausible and energy-efficient solutions faster than learning from scratch.
Results demonstrate that PIRS achieves better energy efficiency while maintaining occupant comfort compared to unguided SAC and traditional control baselines, with quantified improvements in both energy consumption and thermal comfort metrics.