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Found 27 papers, 9 papers with code
Gradable ChatGPT Translation Evaluation
ChatGPT, as a language model based on large-scale pre-training, has exerted a profound influence on the domain of machine translation.
A Covariance Adaptive Student's t Based Kalman Filter
This brief optimizes TKF by using the Gaussian mixture model(GMM), which generates a reasonable covariance matrix from the measurement noise to replace the one used in the existing algorithm and breaks the adjustment limit of the confidence level.
Interactive Model Fusion-Based GM-PHD Filter
In multi-target tracking (MTT), non-Gaussian measurement noise from sensors can diminish the performance of the Gaussian-assumed Gaussian mixture probability hypothesis density (GM-PHD) filter.
Learning to Predict Concept Ordering for Common Sense Generation
Prior work has shown that the ordering in which concepts are shown to a commonsense generator plays an important role, affecting the quality of the generated sentence.
Distributed fusion filter over lossy wireless sensor networks with the presence of non-Gaussian noise
In this paper, a generalized packet drop model is proposed to describe the packet loss phenomenon caused by DoS attacks and other factors.
A Model Fusion Distributed Kalman Filter For Non-Gaussian Observation Noise
A DKF, called model fusion DKF (MFDKF) is proposed against the non-Gaussain noise.
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility
This paper investigates how deep multi-agent reinforcement learning can enable the scalable and privacy-preserving coordination of residential energy flexibility.
Minimum Error Entropy Rauch-Tung-Striebel Smoother
Outliers and impulsive disturbances often cause heavy-tailed distributions in practical applications, and these will degrade the performance of Gaussian approximation smoothing algorithms.
State Estimation of Wireless Sensor Networks in the Presence of Data Packet Drops and Non-Gaussian Noise
Distributed Kalman filter approaches based on the maximum correntropy criterion have recently demonstrated superior state estimation performance to that of conventional distributed Kalman filters for wireless sensor networks in the presence of non-Gaussian impulsive noise.
Curriculum Learning for Relative Overgeneralization
In multi-agent reinforcement learning (MARL), many popular methods, such as VDN and QMIX, are susceptible to a critical multi-agent pathology known as relative overgeneralization (RO), which arises when the optimal joint action's utility falls below that of a sub-optimal joint action in cooperative tasks.
Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scraping
Motivated by how human chemists carry out this process of scraping powder from vials, our work proposes a model-free reinforcement learning method for learning a scraping policy, leading to a fully autonomous sample scraping procedure.
Regularized Softmax Deep Multi-Agent Q-Learning
Tackling overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning, but has received comparatively little attention in the multi-agent setting.
Generalized Minimum Error Entropy for Adaptive Filtering
In our study, a novel concept, called generalized error entropy, utilizing the generalized Gaussian density (GGD) function as the kernel function is proposed.
SA-MATD3:Self-attention-based multi-agent continuous control method in cooperative environments
Cooperative problems under continuous control have always been the focus of multi-agent reinforcement learning.
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients
Policy gradient methods are an attractive approach to multi-agent reinforcement learning problems due to their convergence properties and robustness in partially observable scenarios.
Regularized Softmax Deep Multi-Agent $Q$-Learning
Tackling overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning, but has received comparatively little attention in the multi-agent setting.
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
VDN and QMIX are two popular value-based algorithms for cooperative MARL that learn a centralized action value function as a monotonic mixing of per-agent utilities.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
RODE: Learning Roles to Decompose Multi-Agent Tasks
Learning a role selector based on action effects makes role discovery much easier because it forms a bi-level learning hierarchy -- the role selector searches in a smaller role space and at a lower temporal resolution, while role policies learn in significantly reduced primitive action-observation spaces.
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
We show in particular that this projection can fail to recover the optimal policy even with access to $Q^*$, which primarily stems from the equal weighting placed on each joint action.
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Multi-agent settings in the real world often involve tasks with varying types and quantities of agents and non-agent entities; however, common patterns of behavior often emerge among these agents/entities.
FACMAC: Factored Multi-Agent Centralised Policy Gradients
We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces.
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback.
Optimistic Exploration even with a Pessimistic Initialisation
We show that this scheme is provably efficient in the tabular setting and extend it to the deep RL setting.
VIABLE: Fast Adaptation via Backpropagating Learned Loss
In few-shot learning, typically, the loss function which is applied at test time is the one we are ultimately interested in minimising, such as the mean-squared-error loss for a regression problem.
Interactive Learning of Environment Dynamics for Sequential Tasks
In order for robots and other artificial agents to efficiently learn to perform useful tasks defined by an end user, they must understand not only the goals of those tasks, but also the structure and dynamics of that user's environment.
PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE
Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either discrete or continuous space.
Interactive Learning from Policy-Dependent Human Feedback
This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback.
