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

TL;DR

This paper introduces a novel spatio-temporal value semantics-based abstraction method for deep reinforcement learning in cyber-physical systems, improving decision-making efficiency and generalization in complex environments.

Contribution

It proposes a semantics-based abstraction to construct an abstract MDP for DRL, addressing challenges of uncertainty and data scarcity in dynamic ICPS environments.

Findings

Effective abstraction reduces semantic gaps between models.

Improved decision-making in lane-keeping and cruise control tasks.

Enhanced generalization in complex, dynamic scenarios.

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.