Symbolic visual reinforcement learning: A scalable framework with object-level abstraction and differentiable expression search
Learning efficient and interpretable policies has been a challenging task in reinforcement
learning (RL), particularly in the visual RL setting with complex scenes. While neural …
learning (RL), particularly in the visual RL setting with complex scenes. While neural …
[PDF][PDF] Neuro-Symbolic methods for Trustworthy AI: a systematic review
C Michel-Delétie, MK Sarker - Neurosymbolic …, 2024 - neurosymbolic-ai-journal.com
Recent advances in Artificial Intelligence (AI) especially in deep learning have manifested
an increasing concern in trustworthiness, and its subparts such as interpretability, safety …
an increasing concern in trustworthiness, and its subparts such as interpretability, safety …
Implementability improvement of deep reinforcement learning based congestion control in cellular network
The application of deep reinforcement learning to improve the adaptability of congestion
control is promising. However, the state-of-the-art method indicates a high packet loss and …
control is promising. However, the state-of-the-art method indicates a high packet loss and …
Neuro-Symbolic Computing: Advancements and Challenges in Hardware-Software Co-Design
The rapid progress of artificial intelligence (AI) has led to the emergence of a highly
promising field known as neuro-symbolic (NeSy) computing. This approach combines the …
promising field known as neuro-symbolic (NeSy) computing. This approach combines the …
An Adaptive and Interpretable Congestion Control Service Based on Multi-Objective Reinforcement Learning
The need for an adaptive congestion control (CC) service is crucial due to the heterogeneity
of systems and the diversity of applications. Traditional CC methods often fail to adaptively …
of systems and the diversity of applications. Traditional CC methods often fail to adaptively …
Reinforcement Symbolic Regression Machine
In nature, the behavior of many complex systems can be described by parsimonious math
equations. Symbolic Regression (SR) is defined as the task of automatically distilling …
equations. Symbolic Regression (SR) is defined as the task of automatically distilling …