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1
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics ...
Abstract: This paper focuses on robotic reinforcement learning with sparse rewards for natural language goal representations. An open problem is the sample-inefficiency that stems from the compositionality of natural language, and from the grounding of language in sensory data and actions. We address these issues with three contributions. We first present a mechanism for hindsight instruction replay utilizing expert feedback. Second, we propose a seq2seq model to generate linguistic hindsight instructions. Finally, we present a novel class of language-focused learning tasks. We show that hindsight instructions improve the learning performance, as expected. In addition, we also provide an unexpected result: We show that the learning performance of our agent can be improved by one third if, in a sense, the agent learns to talk to itself in a self-supervised manner. We achieve this by learning to generate linguistic instructions that would have been appropriate as a natural language goal for an originally unintended ... : Preprint ICDL 2022 ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://dx.doi.org/10.48550/arxiv.2204.04308
https://arxiv.org/abs/2204.04308
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2
Towards a self-organizing pre-symbolic neural model representing sensorimotor primitives ...
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3
Incorporating End-to-End Speech Recognition Models for Sentiment Analysis ...
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4
Towards Dialogue-based Navigation with Multivariate Adaptation driven by Intention and Politeness for Social Robots ...
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5
GradAscent at EmoInt-2017: Character- and Word-Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection ...
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6
Interactive Natural Language Acquisition in a Multi-modal Recurrent Neural Architecture ...
Heinrich, Stefan; Wermter, Stefan. - : arXiv, 2017
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