[PDF] Top 20 Transfer in Reinforcement Learning via Shared Features
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Transfer in Reinforcement Learning via Shared Features
... to transfer infor- mation about the optimal value function: if the reward functions in two tasks use different sensors then there is no reason to hope that their value functions contain useful information about ... See full document
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Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus
... style transfer rephrases a text from a source style ...a reinforcement- learning-based generator-evaluator architec- ...to transfer a sentence from the source style to the target ...(sentiment ... See full document
13
Parallel Transfer Learning: Accelerating Reinforcement Learning in Multi Agent Systems
... The shared subspaces are generally of lower dimensionality than source or target and composed of shared ...they transfer information in the Neural Networks space (topologies and weights in an Echo ... See full document
206
A New Reinforcement Learning Method for Train Marshaling Based on the Transfer Distance of Locomotive
... new reinforcement learning system for generating marshaling plan of freight cars in a train is ...total transfer distance of a locomotive is minimized to obtain the desired layout of freight cars for ... See full document
6
Transfer in Deep Reinforcement Learning Using Knowledge Graphs
... cient transfer between deep reinforcement learn- ing agents designed to play text-adventure games, reducing training times and increasing the quality of the learned control ...effectively transfer ... See full document
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A Reinforcement Learning System for Transfer Scheduling of Freight Cars in a Train
... the learning algorithm, so that, at each arrangement on sub track, an evaluation value represents the smallest number of car-movements to achieve the best layout on the main ...The learning algorithm is ... See full document
6
Transfer Learning for Reinforcement Learning Domains: A Survey
... consider learning in a hierarchical Bayesian RL ...tasks. Learning on subsequent tasks shows a clear performance improvement in total reward, and some improvement in ...machine learning settings. ... See full document
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Transfer Learning via Inter-Task Mappings for Temporal Difference Learning
... (TD) learning (Sutton and Barto, 1998) has become a popular reinforcement learning technique in recent ...generalize learning to novel situations, have had some experimental successes and have ... See full document
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Study on Computer Generated Electromagnetic Effects on Computer Users
... the learning point of view. A method used in the transfer learning in single agent learning is not equal to the multi agent ...agent reinforcement learning domain. In the context ... See full document
5
Combined Reinforcement Learning via Abstract Representations
... from the abstract state space, (i) the immediate reward and (ii) value functions over multiple planning depths. The initial predictron architecture was limited to policy evaluation; it was then extended to learn an ... See full document
8
Reinforced Training Data Selection for Domain Adaptation
... The Predictor The Bi-LSTM parser proposed by Kiperwasser and Goldberg (2016) is the predictor. Baselines For dependency parsing, we use the same baselines introduced in the POS tagging task. Results The performance ... See full document
12
Learning to Follow Navigational Directions
... Levit and Roy (2007) developed a spatial seman- tics for the Map Task corpus. They represent instructions as Navigational Information Units, which decompose the meaning of an instruction into orthogonal constituents such ... See full document
9
Heterogeneous Transfer Learning for Image Clustering via the SocialWeb
... text features and image ...bag-of- features representation of images (Li and Perona, 2005) in our ...described via the SIFT descriptors (Lowe, ... See full document
9
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling
... inverse reinforcement learning, we proposed a new framework for data-efficient IRL that leverages the context information of the demonstration set to learn a predictive model of the expert behavior from ... See full document
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Attention-Aware Sampling via Deep Reinforcement Learning for Action Recognition
... Deep reinforcement learning. Reinforcement learning has been originated from the understanding of humans’ decision making process, which aims to enable the agent to decide the behavior from ... See full document
8
Collaborative Multi Agent Dialogue Model Training Via Reinforcement Learning
... We present the first complete attempt at con- currently training conversational agents that communicate only via self-generated lan- guage. Using DSTC2 as seed data, we trained natural language understanding (NLU) ... See full document
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Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach
... achieved via supervised learning (see our supplements for a detailed overview): the resulting models thus learn to identify negation scopes from word-level annotations ... See full document
7
Feature Selection by Singular Value Decomposition for Reinforcement Learning
... In reinforcement learning, function approximation techniques are applied to estimate the value func- ...the features’ ...in learning and make approximation methods more powerful but harder to ... See full document
61
Deep learning for whole slide image analysis : an overview
... artifacts and color variability in staining (32). Artifacts can be introduced throughout the entire sample preparation workflow as well as during the imaging process. These can include ischemia times, fixation times, ... See full document
7
Comparing the Utility of State Features in Spoken Dialogue Using Reinforcement Learning
... of the system, and all possible dialogue paths, were authored by physics experts. Each session consists of an interaction with one student over 5 different college-level physics problems, for a total of 100 di- alogues. ... See full document
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