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Found 8 papers, 4 papers with code
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships
This module incorporates a relation-aware encoder and a feedback training strategy.
Incentive Mechanism and Path Planning for UAV Hitching over Traffic Networks
In Stage I, to deal with the motivations for ground vehicles to assist UAV delivery, a dynamic pricing scheme is proposed to best balance the vehicle response time and payments to ground vehicles.
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
In this work, we introduce a novel interactiOn Pattern disenTangling (OPT) method, to disentangle not only the joint value function into agent-wise value functions for decentralized execution, but also the entity interactions into interaction prototypes, each of which represents an underlying interaction pattern within a subgroup of the entities.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Ask-AC: An Initiative Advisor-in-the-Loop Actor-Critic Framework
Despite the promising results achieved, state-of-the-art interactive reinforcement learning schemes rely on passively receiving supervision signals from advisor experts, in the form of either continuous monitoring or pre-defined rules, which inevitably result in a cumbersome and expensive learning process.
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System
As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system.
Imbalanced Sample Generation and Evaluation for Power System Transient Stability Using CTGAN
This paper proposes a controllable sample generation framework based on Conditional Tabular Generative Adversarial Network (CTGAN) to generate specified transient stability samples.
Distribution Knowledge Embedding for Graph Pooling
A DKEPool network de facto disassembles representation learning into two stages, structure learning and distribution learning.
Noise-induced network bursts and coherence in a calcium-mediated neural network
Noise-induced population bursting has been widely identified to play important roles in the information process.
