Setting the Markov Decision Processes (MDP) Algorithms
Strategy iteration algorithms for games and Markov decision processes
... For Markov Decision Processes We will begin by describing the optimality equations for the average-reward criterion in ...uni-chain setting, the structure of the MDP guarantees that every ...
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Efficient algorithms for budget-constrained Markov decision processes
... action-space Markov decision processes (MDPs) form a classical topic in control, game theory, and learn- ing, and as a result are widely applied, increasingly, in very large-scale ...Many ...
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Markov Decision Processes
... R t+1 S t+1 Figure 3.1: The agent–environment interaction in a Markov decision process. More specifically, the agent and environment interact at each of a sequence of discrete time steps, t = 0, 1, 2, 3, . ...
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Sufficient Markov Decision Processes.
... Data-driven decision support systems are being deployed across a wide range of application domains including medicine, engineering, and ...data-driven decision problems with an infinite or indefinite time ...
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Configurable Markov Decision Processes
... both Markov decision processes with imprecise probabilities and non- stationary Markov decision processes do not admit the pos- sibility to dynamically alter the environmental ...
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Robust Markov Decision Processes
... Although transition sampling has theoretical appeal, it is often prohibitively costly or even infeasible in practice. To obtain independent samples for each state-action pair, one needs to repeatedly direct the MDP into ...
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Some contributions to Markov decision processes
... For every t = 0, 1, 2, . . . , let H t denote the space of admissible trajec- tories up to time t. To put it precisely, H 0 := S, and H t := K × H t−1 when t ≥ 1. Generally speaking, an MDP is a discrete-time ...
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One-Counter Markov Decision Processes
... given probability. Such questions are similar in spirit to questions asked in the rich literature on “adversarial queueing theory” (see, e.g., [4]), although this is a somewhat different setting. These ...
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Scalable Verification of Markov Decision Processes
... Introduction Markov decision processes (MDP) describe systems that interleave nondetermin- istic actions and probabilistic transitions, possibly with rewards or costs assigned to the actions ...
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Compositional Reasoning for Markov Decision Processes
... The rest of this paper is organised as follows. In Section 2 we introduce the model of weighted MDPs, the notation of hyper-derivations and some important properties. Then we define a behavioural preorder based on ...
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Hedging Bets in Markov Decision Processes
... 2 Model We describe here the model of Markov decision processes with alternative objectives (MDPAO). As in a traditional MDP, the process consists of a set of states and a set of actions. At a state, ...
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Markov decision processes with uncertain parameters
... a Markov decision process with coupling constraints, which is often motivated by combining several MDPs into ...combined decision of all agents ...
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Augmenting Markov Decision Processes with Advising
... for setting the balance be- tween “following desirable” and “avoiding undesirable”, a slider for balancing the importance between achieving goals and the tradeoff ...
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Multiple-Environment Markov Decision Processes
... Markov decision processes (MDP) are a standard formalism for modeling systems that ex- hibit both stochastic and non-deterministic ...far. Algorithms for finite state MDPs are known for a ...
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Solving Hybrid Markov Decision Processes
... Abstract. Markov decision processes (MDPs) have developed as a stan- dard for representing uncertainty in decision-theoretic planning. How- ever, MDPs require an explicit representation of the ...
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Bounded-parameter Markov decision processes
... on Markov decision processes; in the following, we mention just a few of the closest such ...expensive algorithms for ...special algorithms to exploit this ...
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Analysis and Simplex-type Algorithms for Countably Infinite Linear Programming Models of Markov Decision Processes.
... two algorithms converge to ...those algorithms proceed, they require transition probabilities and rewards in more ...the algorithms require to ...
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Structural Results for Constrained Markov Decision Processes
... INTRODUCTION Markov Decision Processes (MDPs) have proven to be a useful tool in modelling the dynamic control of service ...inform decision-makers of the class of policies they should ...
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Compositional reasoning for weighted Markov decision processes
... Weighted Markov decision processes (MDPs) have long been used to model quantitative aspects of systems in the presence of uncertainty. However, much of the literature on such MDPs takes a monolithic ...
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Hazard Avoidance Alerting With Markov Decision Processes
... particular Markov state and transition model might describe its behavior well most of the time, but badly on rare occasions, such as when a failure occurs in the ...
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