Publications

Reinforcement learning (RL) is becoming more prevalent in practical domains with human implications, raising ethical questions. Specifically, multi-objective RL has been argued to be an ideal framework for modeling real-world problems and developing human-aligned artificial intelligence. However, the ethical dimension remains underexplored in the field, and no survey covers this aspect. Hence, we propose a review of multi-objective RL from an ethical perspective, highlighting existing works, gaps in the literature, important considerations, and potential areas for future research.

Dynamic colored meshes (DCM) are widely used in various applications; however, these meshes may undergo different processes, such as compression or transmission, which can distort them and degrade their quality. To facilitate the development of objective metrics for DCMs and study the influence of typical distortions on their perception, we create the Tencent - dynamic colored mesh database (TDMD) containing eight reference DCM objects with six typical distortions. Using processed video sequences (PVS) derived from the DCM, we have conducted a large-scale subjective experiment that resulted in 303 distorted DCM samples with mean opinion scores, making the TDMD the largest available DCM database to our knowledge. This database enabled us to study the impact of different types of distortion on human perception and offer recommendations for DCM compression and related tasks. Additionally, we have evaluated three types of state-of-the-art objective metrics on the TDMD, including image-based, point-based, and video-based metrics, on the TDMD. Our experimental results highlight the strengths and weaknesses of each metric, and we provide suggestions about the selection of metrics in practical DCM applications. The TDMD is publicly available at the following location: https://multimedia.tencent.com/resources/tdmd.