Emotion recognition is a crucial task for human conversation understanding.It becomes more challenging with the notion of multimodal data, e.g., language, voice, and facial expressions.As a typical solution, the globaland the local context information are exploited to predict the emotional label for every single sentence, i.e., utterance, in the dialogue.Specifically, the global representation could be captured via modeling of cross-modal interactions at the conversation level.The local one is often inferred using the temporal information of speakers or emotional shifts, which neglects vital factors at the utterance level.Additionally, most existing approaches take fused features of multiple modalities in an unified input without leveraging modalityspecific representations.Motivating from these problems, we propose the Relational Temporal Graph Neural Network with Auxiliary Cross-Modality Interaction (CORECT), an novel neural network framework that effectively captures conversation-level cross-modality interactions and utterance-level temporal dependencies with the modality-specific manner for conversation understanding.Extensive experiments demonstrate the effectiveness of CORECT via its stateof-the-art results on the IEMOCAP and CMU-MOSEI datasets for the multimodal ERC task.

In multitask reinforcement learning, tasks often have sub-tasks that share the same solution, even though the overall tasks are different. If the shared-portions could be effectively identified, then the learning process could be improved since all the samples between tasks in the shared space could be used. In this paper, we propose a Sharing Experience Framework (SEF) for simultaneously training of multiple tasks. In SEF, a confidence sharing agent uses task-specific rewards from the environment to identify similar parts that should be shared across tasks and defines those parts as shared-regions between tasks. The shared-regions are expected to guide task-policies sharing their experience during the learning process. The experiments highlight that our framework improves the performance and the stability of learning task-policies, and is possible to help task-policies avoid local optimums.

Recently, a number of methods have been conducted by combining deep neural network and reinforcement learning to solve problems. Neural networks have strong ability to deal with high dimensional data and a good mean to learn features while reinforcement learning allows a system to learn by experience optimal action control and adjust its behavior to new environments. It thus may be used in mobile robot navigation tasks with perceptron as deep neural network and learning to control by reinforcement learning. We first investigate some issues in agents learning to navigate in indoor environments such as how robot memorize vision information and how it discovers the environments. We show that agents can rely on some position with vision information in navigation task such as reduce training time and locating object efficiently. Some approaches based on deep reinforcement learning are step by step discussed and proposed to deal with each problem in target object navigation task. The key ideas include adding checking point as vision memory and using auxiliary learning task to enhance agents to discover the environment.