Spatio-temporal Value Semantics-based Abstraction for Dense Deep Reinforcement Learning

TL;DR

A semantics-based abstraction is introduced to construct an abstract Markov Decision Process (MDP) for the DRL learning process, aiming to refine the abstract model and mitigate semantic gaps between abstract and concrete states.

Abstract

Intelligent Cyber-Physical Systems (ICPS) represent a specialized form of Cyber-Physical System (CPS) that incorporates intelligent components, notably Convolutional Neural Networks (CNNs) and Deep Reinforcement Learning (DRL), to undertake multifaceted tasks encompassing perception, decision-making, and control. The utilization of DRL for decision-making facilitates dynamic interaction with the environment, generating control actions aimed at maximizing cumulative rewards. Nevertheless, the inherent uncertainty of the operational environment and the intricate nature of ICPS necessitate exploration within complex and dynamic state spaces during the learning phase. DRL confronts challenges in terms of efficiency, generalization capabilities, and data scarcity during decision-making process. In response to these challenges, we propose an innovative abstract modeling approach grounded in spatial-temporal value semantics, capturing the evolution in the distribution of semantic value across time and space. A semantics-based abstraction is introduced to construct an abstract Markov Decision Process (MDP) for the DRL learning process. Furthermore, optimization techniques for abstraction are delineated, aiming to refine the abstract model and mitigate semantic gaps between abstract and concrete states. The efficacy of the abstract modeling is assessed through the evaluation and analysis of the abstract MDP model using PRISM. A series of experiments are conducted, involving diverse scenarios such as lane-keeping, adaptive cruise control, and intersection crossroad assistance, to demonstrate the effectiveness of our abstracting approach.

Figures (7)

• Figure 1: The Framework of Our Approach
• Figure 2: Semantic-based Abstraction for DRL
• Figure 3: ACC Concrete Traces
• Figure 4: ACC Abstract Traces
• Figure 6: ACC

Theorems & Definitions (9)

• definition thmcounterdefinition: Markov Decision Process
• definition thmcounterdefinition: State Value Function $V(s)$
• definition thmcounterdefinition: Action Value Function $Q(s, a)$
• definition thmcounterdefinition: Abstract Markov Decision Process
• definition thmcounterdefinition: Interval Box
• definition thmcounterdefinition: Semantic-based Abstraction MDP
• definition thmcounterdefinition: Spatio-temporal Value Semantics
• definition thmcounterdefinition: ($\varepsilon$,d)-Abstraction
• definition thmcounterdefinition: Spatio-temporal Value Metric