MDPS

The extension of reinforcement learning to MDPs with large state, action space and high complexity has inevitably encountered the problem of the curse of dimensionality, which results in slow convergence and long training time.

传统的强化学习算法应用到大状态、动作空间和任务复杂的马尔可夫决策过程问题时，存在收敛速度慢，训练时间长等问题。

Motivated by the need of practical large-scale Markov systems, we considered in this paper the learning optimization problems for Markov decision processes ( MDPs).

为适应实际大规模Markov系统的需要，讨论Markov决策过程（MDP）基于仿真的学习优化问题。

Discounted and Undiscounted MDPs: a Case Study Based on SARSA(λ) Algorithms

折扣与无折扣MDPs：一个基于SARSA（λ）算法的实例分析

Many sequential decision problems, such as flexible manufacturing systems, traffic command systems and queuing systems etc., can be modeled as Markov decision processes ( MDPs).

实际生活中的许多序贯决策问题，如柔性制造系统、交通指挥系统、排队系统等，都可以模型化为Markov决策过程（MDP）。

The problems on discounting reinforcement learning are analyzed. Several experiments have been performed for comparing the influence of different discounting factors on SARSA(λ) algorithm based on MDPs. The role of average reward scalar to undiscounted SARSA(λ) algorithm is also discussed.

分析了折扣激励学习存在的问题，对MDPs的SARSA（λ）算法进行了折扣的比较实验分析，讨论了平均奖赏常量对无折扣SARSA（（）算法的影响。

S(λ): A reinforcement learning algorithm based on average-payoff MDPs

S（λ）：一个基于平均奖赏MDPs的激励学习算法

This paper elaborates on the low learning efficiency in reinforcement learning due to improper generalization and random exploration policy under deterministic MDPS and proposes a hierarchical reinforcement learning algorithm based on system model.

针对强化学习算法的状态值泛化和随机探索策略在确定性MDP系统控制中存在着学习效率低的问题，本文提出基于模型的层次化强化学习算法。

This article based on the ideas and methods of GIS, connecting with features of mining area disaster designed the Mining area Disaster Production System ( MDPS).

根据地理信息系统的基本思想，结合矿区灾害的特点设计了矿区灾害防治系统（MDPS）。

On-Policy Modeless Reinforcement Learning Algorithms for Average-Payoff MDPs

平均奖赏MDP的在策略无模型激励学习算法

The Effect of Synthetic Muramyl Dipeptide ( MDP) and Its Derivatives ( MDPs) on Immune Function in Mice

胞壁酰二肽及其衍生物对小鼠免疫功能的影响

Parallel Q-learning Algorithms for MDPs Based on Performance Potentials

一种MDP基于性能势的并行Q学习算法

Moderate deviation principles ( MDPs) are proved for the occupation time process of a super Brownian motion with immigration, where the immigration is governed by the Lebesgue measure.

本文证明了当底空间维数d≥3时，一类带移民超布朗运动占位时过程的中偏差，其移民由Lebesgue测度控制。

The concept of Markov performance potentials, which is introduced by Cao, offers a new framework and approach for the optimization of MDPs.

Markov性能势理论的提出，为MDP的优化提供了一种新的理论框架和途径。

Through these solutions we can integrate theory implementation of message transmission based on MDPs, to make it be a safe, stable, maintainable message transmission system.

通过对基于MDPs的消息传递的理论实现进行整合，使之成为安全可靠、性能稳定、可维护的消息传递系统。

In this paper, a neuro-dynamic programming ( NDP) method is discussed via an actor-critic algorithm for Markov decision processes ( MDPs) based on the learning of performance potentials.

研究马尔可大决策过程（MDP）在actor-critic模式下，基于性能势学习的神经元动态规划（NDP）方法。

We discuss the reinforcement learning-based optimization methods of Markov decision processes ( MDPs) using the Markov performance potentials.

在Markov性能势基础上讨论了一种基于强化学习的马尔可夫决策过程（MDP）优化方法。