Jinwen Wang

Jinwen Wang, Youfang Lin, Xiaobo Hu et al.

Visual Reinforcement Learning (VRL) has achieved considerable success in solving control tasks. However, generalizing learned policies to new environments remains a major challenge, as agents often overfit to task-irrelevant features in the training environment. To solve this problem, we introduce the concept of decoupling observations into task-relevant and task-irrelevant representations. Building on this idea, we propose a self-supervised Task-Relevant Representation Decoupling (T2RD) algorithm for VRL. This algorithm consists of three components: task-relevant representation consistency, cross-reconstruction, and cross-dynamic prediction. The first two components achieve the decoupling of content and style features, but the resulting content representations are not necessarily task-relevant. To further refine task-relevant features from content representations, we design the third component that introduces dynamic prediction. T2RD achieves State-Of-The-Art (SOTA) generalization performance and sample efficiency in the DeepMind Control Suite and Robotic Manipulation tasks.

Xiaobo Hu, Youfang Lin, Yue Liu et al.

Visual reinforcement learning has proven effective in solving control tasks with high-dimensional observations. However, extracting reliable and generalizable representations from vision-based observations remains a central challenge. Inspired by the human thought process, when the representation extracted from the observation can predict the future and trace history, the representation is reliable and accurate in comprehending the environment. Based on this concept, we introduce a Bidirectional Transition (BiT) model, which leverages the ability to bidirectionally predict environmental transitions both forward and backward to extract reliable representations. Our model demonstrates competitive generalization performance and sample efficiency on two settings of the DeepMind Control suite. Additionally, we utilize robotic manipulation and CARLA simulators to demonstrate the wide applicability of our method.

Jinwen Wang, Yueqiang Cheng, Qi Li et al.

Intel has introduced a trusted computing technology, Intel Software Guard Extension (SGX), which provides an isolated and secure execution environment called enclave for a user program without trusting any privilege software (e.g., an operating system or a hypervisor) or firmware. Nevertheless, SGX is vulnerable to several side channel attacks (e.g. page-fault-based attack and cache-based attack). In this paper, we explore a new, yet critical side channel attack in SGX, interface-based side channel attack, which can infer the information of the enclave input data. The root cause of the interface-based side channel attack is the input dependent interface invocation information (e.g., interface information and invocation patterns) which can be observed by the untrusted privilege software can reveal the control flow in the enclave. We study the methodology which can be used to conduct the interface-based side channel attack. To illustrate the effectiveness of the interface-based side-channel attacks, we use our methodology to infer whether tracked web pages have been processed by the SGX-assisted NFV platforms and achieve the accuracy of 87.6% and recall of 76.6%. We also identify the packets which belong to the tracked web pages, with the accuracy of 67.9%and recall of 71.1%. We finally propose some countermeasures to defense the interface-based side channel attack in SGX-assisted applications.