scholarly journals Adaptive Honeypot Engagement Through Reinforcement Learning of Semi-Markov Decision Processes

2019 ◽  
pp. 196-216 ◽  
Author(s):  
Linan Huang ◽  
Quanyan Zhu
Keyword(s):  
Reinforcement Learning ◽  
Markov Decision Processes ◽  
Decision Processes ◽  
Markov Decision

scholarly journals Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning

2020 ◽  
Author(s):  
Yu Wang ◽  
Nima Roohi ◽  
Matthew West ◽  
Mahesh Viswanathan ◽  
Geir E. Dullerud
Keyword(s):  
Reinforcement Learning ◽  
Model Checking ◽  
Markov Decision Processes ◽  
Decision Processes ◽  
Markov Decision

Average Reward Reinforcement Learning for Semi-Markov Decision Processes

2017 ◽  
pp. 768-777
Author(s):  
Jiayuan Yang ◽  
Yanjie Li ◽  
Haoyao Chen ◽  
Jiangang Li
Keyword(s):  
Reinforcement Learning ◽  
Markov Decision Processes ◽  
Decision Processes ◽  
Average Reward ◽  
Markov Decision

Reinforcement Learning Based Algorithms for Average Cost Markov Decision Processes

2007 ◽  
Vol 17 (1) ◽  
pp. 23-52 ◽  
Author(s):  
Mohammed Shahid Abdulla ◽  
Shalabh Bhatnagar
Keyword(s):  
Reinforcement Learning ◽  
Markov Decision Processes ◽  
Average Cost ◽  
Decision Processes ◽  
Markov Decision

scholarly journals Sparse Markov Decision Processes With Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning

2018 ◽  
Vol 3 (3) ◽  
pp. 1466-1473 ◽  
Author(s):  
Kyungjae Lee ◽  
Sungjoon Choi ◽  
Songhwai Oh
Keyword(s):  
Reinforcement Learning ◽  
Markov Decision Processes ◽  
Decision Processes ◽  
Tsallis Entropy ◽  
Markov Decision

scholarly journals Configurable Environments in Reinforcement Learning: An Overview

2022 ◽  
pp. 101-113
Author(s):  
Alberto Maria Metelli
Keyword(s):  
Reinforcement Learning ◽  
Markov Decision Processes ◽  
Learning Process ◽  
Real World ◽  
Decision Processes ◽  
Learning Problem ◽  
Complex Control ◽  
Control Frequency ◽  
Markov Decision ◽  
And Control

AbstractReinforcement Learning (RL) has emerged as an effective approach to address a variety of complex control tasks. In a typical RL problem, an agent interacts with the environment by perceiving observations and performing actions, with the ultimate goal of maximizing the cumulative reward. In the traditional formulation, the environment is assumed to be a fixed entity that cannot be externally controlled. However, there exist several real-world scenarios in which the environment offers the opportunity to configure some of its parameters, with diverse effects on the agent’s learning process. In this contribution, we provide an overview of the main aspects of environment configurability. We start by introducing the formalism of the Configurable Markov Decision Processes (Conf-MDPs) and we illustrate the solutions concepts. Then, we revise the algorithms for solving the learning problem in Conf-MDPs. Finally, we present two applications of Conf-MDPs: policy space identification and control frequency adaptation.

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