In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates. This has been a notable problem in training deep RL agents to perform web-based tasks, such as booking flights or replying to emails, where a single mistake can ruin the entire sequence of actions. A common remedy is to "warm-start" the agent by pre-training it to mimic expert demonstrations, but this is prone to overfitting. Instead, we propose to constrain exploration using demonstrations. From each demonstration, we induce high-level "workflows" which constrain the allowable actions at each time step to be similar to those in the demonstration (e.g., "Step 1: click on a textbox; Step 2: enter some text"). Our exploration policy then learns to identify successful workflows and samples actions that satisfy these workflows. Workflows prune out bad exploration directions and accelerate the agent's ability to discover rewards. We use our approach to train a novel neural policy designed to handle the semi-structured nature of websites, and evaluate on a suite of web tasks, including the recent World of Bits benchmark. We achieve new state-of-the-art results, and show that workflow-guided exploration improves sample efficiency over behavioral cloning by more than 100x.
[A]: We solve the sparse rewards problem on web UI tasks using exploration guided by demonstrations

[Q]: We propose an approach for sequence modeling based on autoregressive normalizing flows. Each autoregressive transform, acting across time, serves as a moving reference frame for modeling higher-level dynamics. This technique provides a simple, general-purpose method for improving sequence modeling, with connections to existing and classical techniques. We demonstrate the proposed approach both with standalone models, as well as a part of larger sequential latent variable models. Results are presented on three benchmark video datasets, where flow-based dynamics improve log-likelihood performance over baseline models.
[A]: We show how autoregressive flows can be used to improve sequential latent variable models.

[Q]: It has been an open research challenge for developing an end-to-end multi-domain task-oriented dialogue system, in which a human can converse with the dialogue agent to complete tasks in more than one domain. First, tracking belief states of multi-domain dialogues is difficult as the dialogue agent must obtain the complete belief states from all relevant domains, each of which can have shared slots common among domains as well as unique slots specifically for the domain only. Second, the dialogue agent must also process various types of information, including contextual information from dialogue context, decoded dialogue states of current dialogue turn, and queried results from a knowledge base, to semantically shape context-aware and task-specific responses to human. To address these challenges, we propose an end-to-end neural architecture for task-oriented dialogues in multiple domains. We propose a novel Multi-level Neural Belief Tracker which tracks the dialogue belief states by learning signals at both slot and domain level independently. The representations are combined in a Late Fusion approach to form joint feature vectors of (domain, slot) pairs. Following recent work in end-to-end dialogue systems, we incorporate the belief tracker with generation components to address end-to-end dialogue tasks. We achieve state-of-the-art performance on the MultiWOZ2.1 benchmark with 50.91% joint goal accuracy and competitive measures in task-completion and response generation.
[A]:
We proposed an end-to-end dialogue system with a novel multi-level dialogue state tracker and achieved consistent performance on MultiWOZ2.1 in state tracking, task completion, and response generation performance.