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Title: Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings
This paper proposes the Phy-DRL: a physics-regulated deep reinforcement learning (DRL) framework for safety-critical autonomous systems. The Phy-DRL has three distinguished invariant-embedding designs: i) residual action policy (i.e., integrating data-driven-DRL action policy and physics-model-based action policy), ii) automatically constructed safety-embedded reward, and iii) physics-model-guided neural network (NN) editing, including link editing and activation editing. Theoretically, the Phy-DRL exhibits 1) a mathematically provable safety guarantee and 2) strict compliance of critic and actor networks with physics knowledge about the action-value function and action policy. Finally, we evaluate the Phy-DRL on a cart-pole system and a quadruped robot. The experiments validate our theoretical results and demonstrate that Phy-DRL features guarantee safety compared to purely data-driven DRL and solely model-based design, while offering remarkably fewer learning parameters and fast training towards safety guarantee.  more » « less
Award ID(s):
2311085
PAR ID:
10586705
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
The Twelfth International Conference on Learning Representations
Date Published:
Subject(s) / Keyword(s):
Physics-regulated, Deep Reinforcement Learning
Format(s):
Medium: X
Location:
ienna Austria May 7th, 2024 to May 11th, 2024
Sponsoring Org:
National Science Foundation
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