Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learning

This paper concerns imitation learning (IL) (i.e, the problem of learning to mimic expert behaviors from demonstrations) in cooperative multi-agent systems. The learning problem under consideration poses several challenges, characterized by high-dimensional state and action spaces and intricate inter-agent dependencies. In a single-agent setting, IL has proven to be done efficiently through an inverse soft-Q learning process given expert demonstrations. However, extending this framework to a multi-agent context introduces the need to simultaneously learn both local value functions to capture local observations and individual actions, and a joint value function for exploiting centralized learning. In this work, we introduce a novel multi-agent IL algorithm designed to address these challenges. Our approach enables the centralized learning by leveraging mixing networks to aggregate decentralized Q functions. A main advantage of this approach is that the weights of the mixing networks can be trained using information derived from global states. We further establish conditions for the mixing networks under which the multi-agent objective function exhibits convexity within the Q function space. We present extensive experiments conducted on some challenging competitive and cooperative multi-agent game environments, including an advanced version of the Star-Craft multi-agent challenge (i.e., SMACv2), which demonstrates the effectiveness of our proposed algorithm compared to existing state-of-the-art multi-agent IL algorithms

Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games

Training agents in multi-agent competitive games presents significant challenges due to their intricate nature. These challenges are exacerbated by dynamics influenced not only by the environment but also by opponents' strategies. Existing methods often struggle with slow convergence and instability. To address this, we harness the potential of imitation learning to comprehend and anticipate opponents' behavior, aiming to mitigate uncertainties with respect to the game dynamics. Our key contributions include: (i) a new multi-agent imitation learning model for predicting next moves of the opponents -- our model works with hidden opponents' actions and local observations; (ii) a new multi-agent reinforcement learning algorithm that combines our imitation learning model and policy training into one single training process; and (iii) extensive experiments in three challenging game environments, including an advanced version of the Star-Craft multi-agent challenge (i.e., SMACv2). Experimental results show that our approach achieves superior performance compared to existing state-of-the-art multi-agent RL algorithms

COMMON ERRORS IN PRONOUNCING FINAL CONSONANTS OF ENGLISH-MAJORED SOPHOMORES AT TAY DO UNIVERSITY, VIETNAM

It is not deniable that pronunciation is considered one of the most crucial parts of learning English helping learners enhance their communication in both speaking and listening comprehension. To reach a level of a clear and precise pronunciation has never been an effortless task; however, it is a far more problematic one for English majored students regardless of their learning years. For this reason, the study entitled “Common Errors in Pronouncing Final Consonants of English-Majored Sophomores at Tay Do University” was implemented with the aim at investigating the errors that English-majored students encountered in pronouncing final consonants. 80 English-majored sophomores from course 13 at Tay Do University were selected to participate in the study. Questionnaires and recording tests were delivered to the participants for collecting data and getting more information. The collected data from the two instruments mentioned above were all analyzed afterward. The findings of the research revealed that sophomores of English major often mispronounced the final consonants, particularly /s/, /z/, /ʃ/, /f/ and /v/ in two main mistakes, including omission and substitution. The results of this study may also be useful for those who are interested in this field. Article visualizations

A STUDY ON ERRORS IN PRONOUNCING DENTAL SOUNDS OF ENGLISH-MAJORED SOPHOMORES AT TAY DO UNIVERSITY, VIETNAM

It is not deniable that pronunciation is considered one of the most crucial parts of learning English helping learners enhance their communication in both speaking and listening comprehension. To reach a level of a clear and precise pronunciation has never been an effortless task; however, it is a far more problematic one for English majored students regardless of their learning years. For this reason, the study entitled “Common Errors in Pronouncing Dental Sounds of English-Majored Sophomores at Tay Do University” was implemented with the aim at investigating the errors that English-majored students encountered in pronouncing dental sounds. 80 English-majored sophomores from course 14 at Tay Do University were selected to participate in the study. Questionnaires and recording tests were delivered to the participants for collecting data and getting more information. The collected data from the two instruments mentioned above was all analyzed afterward. The findings of the research revealed that sophomores of English major often mispronounced the dental sounds, including omission and substitution. The results of this study may also be useful for those who are interested in this field. Article visualizations

LAPFormer: A Light and Accurate Polyp Segmentation Transformer

Polyp segmentation is still known as a difficult problem due to the large variety of polyp shapes, scanning and labeling modalities. This prevents deep learning model to generalize well on unseen data. However, Transformer-based approach recently has achieved some remarkable results on performance with the ability of extracting global context better than CNN-based architecture and yet lead to better generalization. To leverage this strength of Transformer, we propose a new model with encoder-decoder architecture named LAPFormer, which uses a hierarchical Transformer encoder to better extract global feature and combine with our novel CNN (Convolutional Neural Network) decoder for capturing local appearance of the polyps. Our proposed decoder contains a progressive feature fusion module designed for fusing feature from upper scales and lower scales and enable multi-scale features to be more correlative. Besides, we also use feature refinement module and feature selection module for processing feature. We test our model on five popular benchmark datasets for polyp segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and ETIS-LaribComment: 7 pages, 7 figures, ACL 2023 underrevie