January 5, 2021
We propose a novel framework to identify subgoals useful for exploration in sequential decision making tasks under partial observability. We utilize the variational intrinsic control framework (Gregor et.al., 2016) which maximizes empowerment – the ability to reliably reach a diverse set of states and show how to identify sub-goals as states with high necessary option information through an information theoretic regularizer. Despite being discovered without explicit goal supervision, our subgoals provide better exploration and sample complexity on challenging grid-world navigation tasks compared to supervised counterparts in prior work.
Written by
Nirbhay Modhe
Prithvijit Chattopadhyay
Mohit Sharma
Abhishek Das
Devi ParikhDhruv BatraRamakrishna VedantamPublisher
International Joint Conference on Artificial Intelligence (IJCAI)
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Ronghang Hu, Amanpreet Singh, Trevor Darrell, Marcus Rohrbach
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The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem.…
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Humans understand novel sentences by composing meanings and roles of core language components. In contrast, neural network models for natural language modeling fail when such compositional generalization is required. The main contribution of…
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Lexicon-free speech recognition naturally deals with the problem of out-of-vocabulary (OOV) words. In this paper, we show that character-based language models (LM) can perform as well as word-based LMs for speech recognition, in word error…
Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert
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