Explainable reinforcement learning: A survey and comparative review
Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …
learning that has attracted considerable attention in recent years. The goal of XRL is to …
A survey on neural-symbolic learning systems
D Yu, B Yang, D Liu, H Wang, S Pan - Neural Networks, 2023 - Elsevier
In recent years, neural systems have demonstrated highly effective learning ability and
superior perception intelligence. However, they have been found to lack effective reasoning …
superior perception intelligence. However, they have been found to lack effective reasoning …
End-to-end symbolic regression with transformers
PA Kamienny, S d'Ascoli, G Lample… - Advances in Neural …, 2022 - proceedings.neurips.cc
Symbolic regression, the task of predicting the mathematical expression of a function from
the observation of its values, is a difficult task which usually involves a two-step procedure …
the observation of its values, is a difficult task which usually involves a two-step procedure …
A unified framework for deep symbolic regression
The last few years have witnessed a surge in methods for symbolic regression, from
advances in traditional evolutionary approaches to novel deep learning-based systems …
advances in traditional evolutionary approaches to novel deep learning-based systems …
Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws
W Tenachi, R Ibata, FI Diakogiannis - The Astrophysical Journal, 2023 - iopscience.iop.org
Symbolic regression (SR) is the study of algorithms that automate the search for analytic
expressions that fit data. While recent advances in deep learning have generated renewed …
expressions that fit data. While recent advances in deep learning have generated renewed …
Generalizing goal-conditioned reinforcement learning with variational causal reasoning
As a pivotal component to attaining generalizable solutions in human intelligence,
reasoning provides great potential for reinforcement learning (RL) agents' generalization …
reasoning provides great potential for reinforcement learning (RL) agents' generalization …
Symbolic regression via neural-guided genetic programming population seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed
output from a black-box process. It is a discrete optimization problem generally believed to …
output from a black-box process. It is a discrete optimization problem generally believed to …
A survey of explainable reinforcement learning
Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …
learning that has attracted considerable attention in recent years. The goal of XRL is to …
Learning to synthesize programs as interpretable and generalizable policies
Recently, deep reinforcement learning (DRL) methods have achieved impressive
performance on tasks in a variety of domains. However, neural network policies produced …
performance on tasks in a variety of domains. However, neural network policies produced …
Symbolic regression via deep reinforcement learning enhanced genetic programming seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed
output from a black-box process. It is a discrete optimization problem generally believed to …
output from a black-box process. It is a discrete optimization problem generally believed to …