The shapley value in machine learning
Over the last few years, the Shapley value, a solution concept from cooperative game theory,
has found numerous applications in machine learning. In this paper, we first discuss …
has found numerous applications in machine learning. In this paper, we first discuss …
A survey on explainable reinforcement learning: Concepts, algorithms, challenges
Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …
A comprehensive survey on multi-agent reinforcement learning for connected and automated vehicles
Connected and automated vehicles (CAVs) require multiple tasks in their seamless
maneuverings. Some essential tasks that require simultaneous management and actions …
maneuverings. Some essential tasks that require simultaneous management and actions …
G2l: Semantically aligned and uniform video grounding via geodesic and game theory
The recent video grounding works attempt to introduce vanilla contrastive learning into video
grounding. However, we claim that this naive solution is suboptimal. Contrastive learning …
grounding. However, we claim that this naive solution is suboptimal. Contrastive learning …
Weightedshap: analyzing and improving shapley based feature attributions
Shapley value is a popular approach for measuring the influence of individual features.
While Shapley feature attribution is built upon desiderata from game theory, some of its …
While Shapley feature attribution is built upon desiderata from game theory, some of its …
Deconfounded value decomposition for multi-agent reinforcement learning
Value decomposition (VD) methods have been widely used in cooperative multi-agent
reinforcement learning (MARL), where credit assignment plays an important role in guiding …
reinforcement learning (MARL), where credit assignment plays an important role in guiding …
An introduction to multi-agent reinforcement learning and review of its application to autonomous mobility
Many scenarios in mobility and traffic involve multiple different agents that need to cooperate
to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning …
to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning …
Explora: Ai/ml explainability for the open ran
The Open Radio Access Network (RAN) paradigm is transforming cellular networks into a
system of disaggregated, virtualized, and software-based components. These self-optimize …
system of disaggregated, virtualized, and software-based components. These self-optimize …
Computation Rate Maximization for SCMA-Aided Edge Computing in IoT Networks: A Multi-Agent Reinforcement Learning Approach
Integrating sparse code multiple access (SCMA) and mobile edge computing (MEC) into the
Internet of Things (IoT) networks can enable efficient connectivity and timely computation for …
Internet of Things (IoT) networks can enable efficient connectivity and timely computation for …
N $\textA^\text2 $ Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning
Value decomposition is widely used in cooperative multi-agent reinforcement learning,
however, its implicit credit assignment mechanism is not yet fully understood due to black …
however, its implicit credit assignment mechanism is not yet fully understood due to black …