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Found 43 papers, 27 papers with code
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games
Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces.
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training
Recent works on sparse neural network training (sparse training) have shown that a compelling trade-off between performance and efficiency can be achieved by training intrinsically sparse neural networks from scratch.
Phrase-level Textual Adversarial Attack with Label Preservation
Generating high-quality textual adversarial examples is critical for investigating the pitfalls of natural language processing (NLP) models and further promoting their robustness.
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics
Recent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics.
A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation
To alleviate the above data issues, we propose a data manipulation method, which is model-agnostic to be packed with any persona-based dialogue generation model to improve its performance.
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games
Text-based games provide an interactive way to study natural language processing.
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL
In this paper, we revisit the theoretical property of GCSL -- optimizing a lower bound of the goal reaching objective, and extend GCSL as a novel offline goal-conditioned RL algorithm.
Goal Randomization for Playing Text-based Games without a Reward Function
In a variety of text-based games, we show that this simple method results in competitive performance for agents.
Lagrangian Generative Adversarial Imitation Learning with Safety
Imitation Learning (IL) merely concentrates on reproducing expert behaviors and could take dangerous actions, which is unbearable in safety-critical scenarios.
Generalization in Text-based Games via Hierarchical Reinforcement Learning
Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection
The ability to detect Out-of-Domain (OOD) inputs has been a critical requirement in many real-world NLP applications.
Safe Exploration by Solving Early Terminated MDP
Safe exploration is crucial for the real-world application of reinforcement learning (RL).
MHER: Model-based Hindsight Experience Replay
Replacing original goals with virtual goals generated from interaction with a trained dynamics model leads to a novel relabeling method, model-based relabeling (MBR).
DAGN: Discourse-Aware Graph Network for Logical Reasoning
The model encodes discourse information as a graph with elementary discourse units (EDUs) and discourse relations, and learns the discourse-aware features via a graph network for downstream QA tasks.
Ranked #17 on
Reading Comprehension
on ReClor
Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation
Dialogue generation models face the challenge of producing generic and repetitive responses.
Self-Supervised Continuous Control without Policy Gradient
Despite the remarkable progress made by the policy gradient algorithms in reinforcement learning (RL), sub-optimal policies usually result from the local exploration property of the policy gradient update.
Learning Predictive Communication by Imagination in Networked System Control
Dealing with multi-agent control in networked systems is one of the biggest challenges in Reinforcement Learning (RL) and limited success has been presented compared to recent deep reinforcement learning in single-agent domain.
REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement
It is crucial since the quality of the evidence is the key to answering commonsense questions, and even determines the upper bound on the QA systems performance.
TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game
We show that with orders of less computation scale, a faithful reimplementation of AlphaStar's methods can not succeed and the proposed techniques are necessary to ensure TStarBot-X's competitive performance.
TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning
This poses non-trivial difficulties for researchers or engineers and prevents the application of MARL to a broader range of real-world problems.
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language.
On the Guaranteed Almost Equivalence between Imitation Learning from Observation and Demonstration
By contrast, this paper proves that LfO is almost equivalent to LfD in the deterministic robot environment, and more generally even in the robot environment with bounded randomness.
Pretrained Language Models for Dialogue Generation with Multiple Input Sources
In this work, we study dialogue models with multiple input sources adapted from the pretrained language model GPT2.
Zeroth-Order Supervised Policy Improvement
However, PG algorithms rely on exploiting the value function being learned with the first-order update locally, which results in limited sample efficiency.
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning
A great challenge in cooperative decentralized multi-agent reinforcement learning (MARL) is generating diversified behaviors for each individual agent when receiving only a team reward.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Curriculum-guided Hindsight Experience Replay
This ``Goal-and-Curiosity-driven Curriculum Learning'' leads to ``Curriculum-guided HER (CHER)'', which adaptively and dynamically controls the exploration-exploitation trade-off during the learning process via hindsight experience selection.
Unsupervised Domain Adaptation on Reading Comprehension
On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains.
A Corpus-free State2Seq User Simulator for Task-oriented Dialogue
The framework builds a user simulator by first generating diverse dialogue data from templates and then build a new State2Seq user simulator on the data.
Learning to Solve a Rubik's Cube with a Dexterous Hand
We present a learning-based approach to solving a Rubik's cube with a multi-fingered dexterous hand.
Robotics
Arena: a toolkit for Multi-Agent Reinforcement Learning
We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research.
Revisiting Metric Learning for Few-Shot Image Classification
However, the importance of feature embedding, i. e., exploring the relationship among training samples, is neglected.
Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition
We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER).
DHER: Hindsight Experience Replay for Dynamic Goals
Dealing with sparse rewards is one of the most important challenges in reinforcement learning (RL), especially when a goal is dynamic (e. g., to grasp a moving object).
BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering
Graph convolutional networks are used to obtain a relation-aware representation of nodes for entity graphs built from documents with multi-level features.
Towards Query Efficient Black-box Attacks: An Input-free Perspective
First, we initialize an adversarial example with a gray color image on which every pixel has roughly the same importance for the target model.
A Deep Network for Arousal-Valence Emotion Prediction with Acoustic-Visual Cues
In this paper, we comprehensively describe the methodology of our submissions to the One-Minute Gradual-Emotion Behavior Challenge 2018.
Learning how to Active Learn: A Deep Reinforcement Learning Approach
Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate.
Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary
Cross-lingual model transfer is a compelling and popular method for predicting annotations in a low-resource language, whereby parallel corpora provide a bridge to a high-resource language and its associated annotated corpora.
Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem.
Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection
Cross lingual projection of linguistic annotation suffers from many sources of bias and noise, leading to unreliable annotations that cannot be used directly.
Saliency Propagation From Simple to Difficult
In the teaching-to-learn step, a teacher is designed to arrange the regions from simple to difficult and then assign the simplest regions to the learner.
Transfer Learning across Networks for Collective Classification
In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks.
