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Found 17 papers, 10 papers with code
Open Ad Hoc Teamwork with Cooperative Game Theory
Open ad hoc teamwork further complicates this challenge by considering environments with a changing number of teammates, referred to as open teams.
Shapley Value Based Multi-Agent Reinforcement Learning: Theory, Method and Its Application to Energy Network
We first extend a game model called convex game and a payoff distribution scheme called Shapley value in cooperative game theory to Markov decision process, named as Markov convex game and Markov Shapley value respectively.
Aligning Individual and Collective Objectives in Multi-Agent Cooperation
The visualization of learning dynamics effectively demonstrates that AgA successfully achieves alignment between individual and collective objectives.
E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning
Successful machine learning involves a complete pipeline of data, model, and downstream applications.
Invariant Learning via Probability of Sufficient and Necessary Causes
To capture the information of sufficient and necessary causes, we employ a classical concept, the probability of sufficiency and necessary causes (PNS), which indicates the probability of whether one is the necessary and sufficient cause.
Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition
In this paper, a semi-supervised Dual-stream Self-Attentive Adversarial Graph Contrastive learning framework (termed as DS-AGC) is proposed to tackle the challenge of limited labeled data in cross-subject EEG-based emotion recognition.
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction
Centralised training with decentralised execution (CT-DE) serves as the foundation of many leading multi-agent reinforcement learning (MARL) algorithms.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularization
To deal with unknown uncertainty sets, we further propose a novel adversarial approach to generate them based on the value function.
Learning to Estimate and Refine Fluid Motion with Physical Dynamics
General optical flow methods are typically designed for rigid body motion, and thus struggle if applied to fluid motion estimation directly.
Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach
However, the uncontrollable false positive rate of the data-driven detector and the extra cost of frequent MTD usage limit their wide applications.
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks
This paper presents a problem in power networks that creates an exciting and yet challenging real-world scenario for application of multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning
This paper studies a theoretical framework for value factorisation with interpretability via Shapley value theory.
Learning to Shape Rewards using a Game of Two Partners
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards.
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System
We test HDNO on MultiWoz 2. 0 and MultiWoz 2. 1, the datasets on multi-domain dialogues, in comparison with word-level E2E model trained with RL, LaRL and HDSA, showing improvements on the performance evaluated by automatic evaluation metrics and human evaluation.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Shapley Q-value: A Local Reward Approach to Solve Global Reward Games
To deal with this problem, we i) introduce a cooperative-game theoretical framework called extended convex game (ECG) that is a superset of global reward game, and ii) propose a local reward approach called Shapley Q-value.
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
We propose a new sampling method, the thermostat-assisted continuously-tempered Hamiltonian Monte Carlo, for Bayesian learning on large datasets and multimodal distributions.
Spatio-temporal Aware Non-negative Component Representation for Action Recognition
This paper presents a novel mid-level representation for action recognition, named spatio-temporal aware non-negative component representation (STANNCR).
