Where does Markov decision model is used?
Answers from top 9 papers
More filters
| Papers (9) | Insight |
|---|---|
2.3K Citations | Markov models are useful when a decision problem involves risk that is continuous over time, when the timing of events is important, and when important events may happen more than once. |
11 Apr 2009 30 Citations | It is generic and could be applied as well for analyzing a given situation of a Markov Decision Process. |
27 Aug 2005 23 Citations | Also designed is a novel interpretation of Markov decision process providing clear mathematical formulation to connect reinforcement learning as well as to express integrated agent system. |
01 Dec 2014 31 Citations | Third, it provides applications to control of partially observable Markov decision processes and, in particular, to Markov decision models with incomplete information. |
15 Jul 2019 | On the other hand, it is the first practical algorithm even for Markov decision processes. |
19 May 2004 15 Citations | In this study, we propose Markov decision processes as an alternative to the action cost functions approach. |
12 Citations | Finally, these methods appear likely to unify some diverse results in Markov decision theory. The results in this paper are very general. |
17 Oct 2016 | It in particular provides the first sound and feasible method for performing parameter synthesis of Markov decision processes. |
| In this sense they generalize some wellknown results for Markov decision processes with finite or compact action space. |
Related Questions
How do markov chains work?4 answersMarkov chains are sequences of random variables where the future value of a variable depends only on its present value and is independent of its past. They are commonly used in modeling real-world systems with uncertainty. Markov chains can be discrete or continuous, depending on the time parameter. In discrete time, the concept of reversible Markov chains is introduced, where a stable Markov chain follows the same distribution as its time-reversible chain. Markov chains can also be represented as random walks on directed graphs, where the limiting behavior is determined by the cycles in the graph. In continuous time, Markov processes are used, and the holding time in a state follows an exponential distribution. Multiplex networks introduce a "Markov chains of Markov chains" model, where random walkers can remain in the same layer or move to different layers, leading to novel phenomena such as multiplex imbalance and multiplex convection. Markov processes are commonly used for modeling phenomena where the future values depend only on the immediately preceding state, and they can be characterized by the set of possible states and the stationary probabilities of transition between these states.
What is Markov Decision Process?3 answersA Markov Decision Process (MDP) is a mathematical framework used to model decision-making in situations where outcomes are partially random and partially under the control of a decision-maker. It consists of key components and can be extended with various models. Common solutions to MDP problems include linear programming, value iteration, policy iteration, and reinforcement learning. MDPs are used to study decision-making in individuals with self-control problems, incorporating ideas from psychological research and economics. They explore inter-temporal decision-making with present bias and the impact on well-being. MDPs are also applied to response adaptive clinical trials, where the treatment allocation process is formulated as a stochastic sequential decision problem. An algorithm is proposed to approximate the optimal value, and the average reward under the identified policy converges to the optimal value. MDPs are used for modeling systems with non-deterministic and probabilistic behavior, and the state space explosion problem is addressed by exploiting a hierarchical structure with repetitive parts. This approach accelerates analysis by treating subroutines as uncertain and abstracting them into a parametric template.
What is Q-learning and Markov Decision Process?4 answersQ-learning is a reinforcement learning method used to estimate an optimal decision strategy in a decision problem. It is a general class of methods that includes Temporal-difference (TD) learning, which is a computationally efficient framework for model-free reinforcement learning. Q-learning enables an agent to learn the optimal action-value function, also known as the Q-value function, which represents the expected cumulative reward for taking a particular action in a given state. Markov Decision Process (MDP) is a mathematical framework used to model decision-making problems with sequential interactions. It assumes that the future state depends only on the current state and action, and not on the history of states and actions. In the context of Q-learning, it has been proven to converge on MDPs and Q-uniform abstractions of finite-state MDPs.
What are the key challenges in applying Markov decision processes to real-world problems?4 answersThe key challenges in applying Markov decision processes (MDP) to real-world problems include the perception that MDP is computationally prohibitive, its notational complications and conceptual complexity, and the sensitivity of optimal solutions to estimation errors in state transition probabilities. Additionally, for certain optimization problems in MDP, such as the finite horizon problem and the percentile optimization problem, dynamic programming is not applicable, leading to NP-hardness results. However, recent developments in approximation techniques and increased numerical power have addressed some of the computational challenges. Furthermore, MDP offers the ability to develop approximate and simple practical decision rules and provides a probabilistic modeling approach for practical problems. By incorporating robustness measures, such as using uncertainty sets with statistically accurate representations, the limitations of estimation errors can be mitigated with minimal additional computing cost.
Are Markov chains Bayesian?5 answers
What is role of Markov decision process in reinforcement learning?5 answers