Hybrid Energy-Aware Reward Shaping: A Unified Lightweight Physics-Guided Methodology for Policy Optimization
arXiv:2603.11600v2 Announce Type: replace Abstract: Deep reinforcement learning for continuous control often suffers from high variance, low energy efficiency, and poor generalization under distribution shift, as purely data-driven exploration ignores available physical structure. This paper proposes Hybrid Energy-Aware Reward Shaping (H-EARS), which encodes dominant energy terms -- assumed known a priori -- directly as reward potentials at O(n) per-step computation. H-EARS decomposes the shaping potential into task-oriented and energy-based components, supplemented by an action regularization
The continuous push for more efficient and robust deep reinforcement learning (DRL) applications, particularly in robotics and autonomous systems, necessitates advancements addressing current limitations like energy consumption and generalization.
This research provides a methodology to significantly improve the energy efficiency, stability, and generalization of DRL algorithms in continuous control tasks, enabling more practical and reliable real-world deployments.
Optimizing DRL with physics-guided reward shaping changes how AI models learn complex physical interactions, moving from purely data-driven to knowledge-augmented approaches for better performance and resource use.
- · Robotics industry
- · Autonomous systems developers
- · Energy-efficient AI hardware manufacturers
- · Industrial automation
- · Developers relying solely on brute-force, data-intensive DRL
- · Systems with high energy constraints unable to utilize current DRL
- · Those slow to integrate physics-informed AI methods
More energy-efficient and generalizable AI policies will accelerate the development of complex robotic systems.
The reduced computational and energy demands could broaden the accessibility of advanced DRL for smaller enterprises and edge devices.
This could lead to a wave of innovation in fields requiring precise, energy-constrained physical control, fostering new classes of automated machines.
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Read at arXiv cs.LG