[PDF] Top 20 Reinforcement Learning With High-Level Task Specifications
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Reinforcement Learning With High-Level Task Specifications
... logic specifications are ...as task description and suits the need in many ...and task rules may be known and expressed as specifications in temporal ...vehicles, specifications imply ... See full document
173
Extraversion differentiates between model based and model free strategies in a reinforcement learning task
... for learning from reward prediction errors, and it has been suggested that variations in this mechanism are reflected in personality factors such as trait ...reward learning, it is not yet known whether ... See full document
10
Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach
... Evaluation. Given the considerable inconsis- tencies in human annotations of negation scopes and the lack of universal rules, we regard the “true” negation scopes as unobservable. Hence, we later compare the identified ... See full document
7
Application of Reinforcement Learning on High Speed Rail Cognitive Radio
... The Global System for Mobile Communications Railway (GSM-R) implemented today is based on GSM which is designed for the need of special railway features [6]. The GSM system is dedicated to provide the bidirectional radio ... See full document
5
Comparing Action as Input and Action as Output in a Reinforcement Learning Task
... To ensure that the robot has a higher chance of reaching the goal, the distance to the goal was reduced so that it was as short as feasibly possible. This was done after the observation that the further the robot is from ... See full document
5
Learning High Level Planning from Text
... low- level planning actions in the world, as can be seen in Figure ...a high-level description of world dynamics, it does not provide sufficient details for successful plan ... See full document
10
Composite Task Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
... the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep rein- forcement learning approach to learning a dialogue manager that ... See full document
10
Hierarchical Reinforcement Learning and Hidden Markov Models for Task Oriented Natural Language Generation
... likely sequence independent of content selection. The valid sequence policy generates any grammat- ical sequence. All policies were trained for 20000 episodes. 9 Figure 3, which plots the average re- wards of all three ... See full document
6
What is Acceptably Safe for Reinforcement Learning?
... Machine Learning algorithms are becoming more prevalent in critical systems where dynamic decision making and efficiency are the ...of Reinforcement Learning in particu- lar, considering the ... See full document
14
Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement Learning
... In the context of deep RL, several works have explored the problem of transfer learning. For example, Rusu et al. [25] propose policy distillation, where the Q-values learned in the target policy are used as ... See full document
13
Multi-Task Deep Reinforcement Learning with PopArt
... multi-task reinforcement learning that have been explored in the literature: off-policy learning of many predictions about the same stream of experience (Schmidhuber 1990; Sutton et ...of ... See full document
8
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 of ... See full document
5
Concurrent task performance enhances low level visuomotor learning
... arousal level may have also been automatically increased as task difficulty was ...arousal level was therefore ad- dressed in Experiment 2 by including three conditions in which arousal was increased ... See full document
10
Optimising Turn Taking Strategies With Reinforcement Learning
... turn-taking task is separated from the common dialogue management one and it is handled by a separated module called the Sched- uler (Khouzaimi et ...uses reinforcement learn- ing to optimise turn-taking ... See full document
10
Task Oriented Query Reformulation with Reinforcement Learning
... Same query, different tasks We compare in Ta- ble 5 the reformulation of a sample query made by models trained on different datasets. The model trained on TREC-CAR selects terms that are sim- ilar to the ones in the ... See full document
10
Deep Imitation Learning for 3D Navigation Tasks
... proposed learning method is generic and doesn’t require any prior knowledge of the ...active learning is employed to adapt to situations that are not represented in the ...proposed learning from ... See full document
28
Using Reinforcement Learning for Dialogue Act Classification in Task oriented Conversation Systems
... All the sentences in conversations represent some kind of intention of the speaker, understanding the speaker's purpose leads to high-quality and effective conversations. For the above reasons, DA becomes one of ... See full document
10
Sink or swim? Using assessment criteria to help students reach their potential
... The first assignment therefore, is a vital step along the way of academic progression and national developments have encouraged universities to provide transparent assessment information. However, Rust et al (2003) ... See full document
10
Sequential Decision Task by Adaptive Reinforcement Learning Method
... Stefanos Doltsinis Pyeatt [9] et al. through this paper approached ramp-up as a sequential adjustment and tuning process to a desirable performance manufacturing system in the fastest possible time. Focusing on ... See full document
5
Athanor : high level local search over abstract constraint specifications in Essence
... the high level, nested types in E SSENCE , such as set of partitions or multiset of sequences, without refin- ing such types into low level ...the high level types allows high ... See full document
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