Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings
International Conference on Learning Representations(2023)
Abstract
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 guaranteed safety compared to purely data-driven DRL and solely
model-based design while offering remarkably fewer learning parameters and fast
training towards safety guarantee.
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