Sep 18, 2016 · Abstract:Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, ...
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Oct 1, 2016 · The hybrid neural- symbolic reinforcement learning architecture we propose relies on a deep learning solution to the symbol grounding problem.
It is shown that the resulting system -- though just a prototype -- learns effectively, and, by acquiring a set of symbolic rules that are easily ...
Oct 12, 2016 · The main idea is to learn several Q functions for the different interactions and query those that are relevant for the current situation. Given ...
Oct 12, 2016 · TLDR; the paper's authors suggest and demonstrate an AI architecture that takes advantage of how humans think, and how our brains are organized.
Deep Symbolic Reinforcement Learning (DSRL) seeks to incorporate such capacities to deep Q-networks (DQN) by learning a relevant symbolic representation prior ...
A novel extension of DSRL is proposed, which is called Symbolic Reinforcement Learning with Common Sense (SRL+CS), offering a better balance between ...
People do planning or think complex problems via first make the perception abstract and tackle it. This paper explores the how to first symbolize the raw ...
Our 2016 paper "Towards Deep Symbolic Reinforcement Learning" not only showed how to get the best of both the DRL and symbolic AI worlds, it was also ...
Combining Reinforcement Learning with Neuro-Symbolic Planning
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Dec 18, 2022 · Towards Deep Symbolic Reinforcement Learning. The method in this paper takes some aspects from machine learning, and combine them with ...