Multi-agent learning plays an increasingly important role in solving complex dynamic problems in today's society. Recently, an evolutionary game theoretic approach to multi-agent reinforcement learning has been pr...
详细信息
Multi-agent learning plays an increasingly important role in solving complex dynamic problems in today's society. Recently, an evolutionary game theoretic approach to multi-agent reinforcement learning has been proposed as a first step towards a more general theoretical framework. This article uses the evolutionary game theory perspective to link behavioral properties of learning algorithms to their performance in both homogeneous and heterogeneous games, thereby contributing to a better understanding of multiagent reinforcement learning dynamics. Simulation experiments are performed in the domain of 2 × 2 normal form games with the learning algorithms Lenient and non-lenient Frequency Adjusted Q-learning, Finite Action-set learning Automata and Polynomial Weights Regret Minimization. The results show that evolutionary game theory provides an efficient way to predict the behavior, convergence properties and performance of reinforcement learners. In general, leniency is found to be the preferable choice in cooperative games. Furthermore, the non-lenient learning algorithms do not show significant differences when their intrinsic learning speed is compensated for.
Electronic medical record (EMR) is the core data which generated in the process of clinical treatment. By extracting the record text’s entity relationship, we can catch a large amount of information closely related t...
详细信息
This paper presented a novel approach to malicious URL detection, utilizing various Deep learning techniques including Simple RNN, LSTM, and GRU. The experiments were conducted on a malicious URL dataset sourced from ...
详细信息
We establish finite-sample guarantees for a polynomial-time algorithm for learning a nonlinear, nonparametric directed acyclic graphical (DAG) model from data. The analysis is model-free and does not assume linearity,...
详细信息
Subgraph counting—the task of determining the number of instances of a query pattern within a large graph—lies at the heart of many critical applications, from analyzing financial networks and transportation systems...
详细信息
The increasingly frequent occurrence of natural disasters and industrial accidents has heightened the demand for efficient and dependable rescue operations. In this paper, we introduce map-sharing multi-agent proximal...
详细信息
In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative entropy between two multivariate Gaussi...
详细信息
ISBN:
(纸本)9781595937933
In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the distance function. We express this problem as a particular Bregman optimization problem - -that of minimizing the LogDet divergence subject to linear constraints. Our resulting algorithm has several advantages over existing methods. First, our method can handle a wide variety of constraints and can optionally incorporate a prior on the distance function. Second, it is fast and scalable. Unlike most existing methods, no eigenvalue computations or semi-definite programming are required. We also present an online version and derive regret bounds for the resulting algorithm. Finally, we evaluate our method on a recent error reporting system for software called Clarify, in the context of metric learning for nearest neighbor classification, as well as on standard data sets.
By exploiting the properties of superposition and entanglement found in quantum systems Quantum Computation has been applied to the design of algorithms considerably more efficient than the known classical ones. Known...
详细信息
Assessing asthma severity is inherently difficult because it is highly subjective, often overlapping with symptoms of a common cold, and few objective tools currently exist for it. Our long-term goal is to empower par...
详细信息
The applications of machine learning have now reached variety of industries, including banking and financial organisations. While credit approval is a key concern of the banking industry, machine learning is widely re...
详细信息
暂无评论