Mar 3, 2021 · The LNNs functions as an end-to-end differentiable network that minimizes a novel contradiction loss to learn interpretable rules. In this paper ...
Abstract. Conventional deep reinforcement learning methods are sample-inefficient and usually require a large number of training trials before convergence.
This paper proposes an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained ...
The LNNs functions as an end-to-end differentiable network that minimizes a novel contradiction loss to learn interpretable rules. In this paper, we utilize ...
May 28, 2021 · Abstract We propose a novel reinforcement learning method which uses fixed Logical Neural Networks and trainable traditional neural network to ...
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... Training Logical Neural Networks by Primal–Dual Methods for Neuro ... External Knowledge by using Logical Neural Networks , KBRL Workshop at IJCAI-PRICAI 2020.
Reinforcement learning with external knowledge by using logical neural networks. ... nawar, A., Gray, A.: Neuro-symbolic reinforcement learning with first-order ...
Feb 1, 2023 · The proposed method is orthogonal to training algorithms, and the external knowledge can be flexibly recomposed, rearranged, and reused in both ...
Apr 24, 2019 · To address these two challenges, we propose a novel algorithm named Neural Logic Reinforcement Learning (NLRL) to represent the policies in ...
Missing: External | Show results with:External
Nov 7, 2021 · The paper explains an algorithm to extract first-order logical facts from given textual ob- servation by using the agent history and Con-.