TextGAIL: Generative Adversarial Imitation Learning for Text Generation

Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs' inferior performance is due to the lack of a reliable guiding signal in their discriminators. To address this problem, we propose a generative adversarial imitation learning framework for text generation that uses large pre-trained language models to provide more reliable reward guidance. Our approach uses contrastive discriminator, and proximal policy optimization (PPO) to stabilize and improve text generation performance. For evaluation, we conduct experiments on a diverse set of unconditional and conditional text generation tasks. Experimental results show that TextGAIL achieves better performance in terms of both quality and diversity than the MLE baseline. We also validate our intuition that TextGAIL's discriminator demonstrates the capability of providing reasonable rewards with an additional task.Comment: AAAI 202

Cu-Based Electrocatalysts for Carbon Dioxide Conversion to Value-Added Chemicals

Massive usage of fossil fuel has being causing considerable emission of CO2, which increases the temperature of the planet and greatly threaten human living environment, such as soil degradation, lower agricultural productivity, desertification, less biodiversity, fresh-water reduction, ocean acidification, ozone sphere destruction, etc. A number of technologies are being developed to reduce the CO2 amount, however, all existing technologies except utilizing CO2 as a feedstock, are hardly to essentially close the anthropogenic carbon loop. Currently, considering the economy and operability, electroreduction of CO2 seems to be the most promising strategy to convert CO2 to high value chemicals. During the process of CO2 electroreduction, Cu-based catalysts become the most popular because they meet the requirements of activating CO2 and intermediates, suppression of hydrogen formation, and electron transportation. Herein, the factors that affect the Cu-based catalysts’ performance, including morphology, particle sizes, presence of atomic-scale defects, surface roughness, residual oxygen atoms, and so on, have been surveyed and discussed. In addition, the most probable reaction pathways to synthesize the desirable C2 products under different situation have been identified, which follow *CO + *CO → *COCO, *CO + *COH → C2, *CO + *CHO → C2 and *COH → *CH2 → C2. This report will benefit the design and optimization of Cu-based catalysts for the conversion of CO2 to high value chemicals with high efficiency and selectivity

Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning

A fundamental question in any peer-to-peer ride-sharing system is how to, both effectively and efficiently, meet the request of passengers to balance the supply and demand in real time. On the passenger side, traditional approaches focus on pricing strategies by increasing the probability of users' call to adjust the distribution of demand. However, previous methods do not take into account the impact of changes in strategy on future supply and demand changes, which means drivers are repositioned to different destinations due to passengers' calls, which will affect the driver's income for a period of time in the future. Motivated by this observation, we make an attempt to optimize the distribution of demand to handle this problem by learning the long-term spatio-temporal values as a guideline for pricing strategy. In this study, we propose an offline deep reinforcement learning based method focusing on the demand side to improve the utilization of transportation resources and customer satisfaction. We adopt a spatio-temporal learning method to learn the value of different time and location, then incentivize the ride requests of passengers to adjust the distribution of demand to balance the supply and demand in the system. In particular, we model the problem as a Markov Decision Process (MDP)