[PDF] Top 20 Deep Reinforcement Learning with a Natural Language Action Space
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Deep Reinforcement Learning with a Natural Language Action Space
... a natural language action space in sequential text understanding: the deep reinforcement rele- vance network ...for action text embeddings, which are combined using a ... See full document
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Language Understanding for Text based Games using Deep Reinforcement Learning
... use deep reinforcement learning for training, our work has important ...an action selector is useful for transferring the learnt representations to new game ... See full document
11
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference
... Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is one of the most important prob- lems in natural language ...have deep connections with the ... See full document
11
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
... maps natural language utterances into ex- ecutable programs when only indirect su- pervision is available: examples are la- beled with the correct execution result, but not the program ...the space ... See full document
12
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action Oriented and Goal Oriented Instructions
... ral language instructions on a robot platform (Vo- gel and Jurafsky, 2010; Tellex et ...using language and expert trajectories based rewards, which allow for planning within a stochastic environment along ... See full document
9
Paraphrase Generation with Deep Reinforcement Learning
... in natural language processing ...a deep reinforce- ment learning approach to paraphrase gener- ...to-sequence learning model, can produce para- phrases given a ...a deep ... See full document
14
Querying NoSQL with Deep Learning to Answer Natural Language Questions
... Almost all of today’s knowledge is stored in databases and thus can only be accessed with the help of domain spe- cific query languages, strongly limiting the number of peo- ple which can access the data. In this work, ... See full document
6
Sentence Simplification with Deep Reinforcement Learning
... Presented in its original form, the REINFORCE algorithm starts learning with a random policy. This assumption can make model training chal- lenging for generation tasks like ours with large vocabularies (i.e., ... See full document
11
Deep Reinforcement Learning for Dialogue Generation
... of reinforcement learning, which have been widely ap- plied in MDP and POMDP dialogue systems (see Re- lated Work section for ...ral reinforcement learning (RL) generation method, which can ... See full document
11
Deep Reinforcement Learning for Swarm Systems
... in deep reinforcement learning for swarms and multi-agent systems in ...many-agent reinforcement learning platform based on a multi-channel image state representation, which uses ... See full document
31
Exploring Deep Reinforcement Learning with Multi Q Learning
... back-propagation. Deep learning is a variety of artificial neural networks and has seen great success in learning from high-dimensional data, specifically image recognition [8], Natural ... See full document
16
Convolutional Neural Networks for Sentence Classification
... Deep learning models have achieved remarkable results in computer vision (Krizhevsky et ...Within natural language process- ing, much of the work with deep learning meth- ods has ... See full document
6
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
... Reinforcement Learning. Deep Deterministic Policy Gra- dient algorithm (DDPG) (Lillicrap et ...using deep neural networks to approximate the action-value function for improving ... See full document
8
Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
... To come up with a general model for handling a combinatorial action-value function, we further pro- pose the DRRN-BiLSTM (Figure 2(d)). In this ar- chitecture, we use a DNN to generate an embedding for each ... See full document
11
What Do We Learn from Word Associations? Evaluating Machine Learning Algorithms for the Extraction of Contextual Word Meaning in Natural Language Processing
... Keywords: Machine Learning; Algorithms; Natural Language Processing, Deep Learning, Vector 29.. Space Models, Semantic Similarity, Distributional Semantics, Latent Semantic Analys[r] ... See full document
21
Deep Reinforcement Learning for Drone Delivery
... drones. Reinforcement learning (RL) is the branch of artificial intelligence able to train ...machines. Reinforcement learning is inspired by a human’s way of learning, based on trial ... See full document
19
Self reflective deep reinforcement learning
... self-reflective learning model that depends of deep combined actor-critic layered architecture has been ...the learning process for successful experience or forgetting it for bad ... See full document
7
Mathematical Reinforcement to the Minibatch of Deep Learning
... In this paper, we treated the minibatch of Deep Learning and gave the mathematical reinforcement to it from the viewpoint of Linear Algebra and presented some related problems. We expect that young ... See full document
14
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning
... We present a novel approach for clinical diagno- sis inferencing that mimics the cognitive process of clinicians using deep reinforcement learning via leveraging evidence from external resources. Our ... See full document
11
Automatic Text Summarization Using Reinforcement Learning with Embedding Features
... In this work, we apply the Deep Q-Networks (DQN)-based model (Volodymyr et al. 2015) to automatic text summarization tasks. In the case of text summarization, the state denotes a summary which can still be ... See full document
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