Markov decision process (MDP), also called a stochastic dynamic program or stochastic control problem, is a model for sequential decision making when...

observable Markov decision process (POMDP) is a generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it...

In probability theory and statistics, a Markov chain or Markov process is a stochastic process describing a sequence of possible events in which the probability...

to expected rewards. A partially observable Markov decision process (POMDP) is a Markov decision process in which the state of the system is only partially...

probability theory and statistics, the term Markov property refers to the memoryless property of a stochastic process, which means that its future evolution...

The decentralized partially observable Markov decision process (Dec-POMDP) is a model for coordination and decision-making among multiple agents. It is a...

Markov decision process Markov's inequality Markov brothers' inequality Markov information source Markov network Markov number Markov property Markov process...

dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dynamic programming...

Gauss–Markov theorem Gauss–Markov process Markov blanket Markov boundary Markov chain Markov chain central limit theorem Additive Markov chain Markov additive...

this choice by trying both directions over time. For any finite Markov decision process, Q-learning finds an optimal policy in the sense of maximizing...

theory, a Markov reward model or Markov reward process is a stochastic process which extends either a Markov chain or continuous-time Markov chain by adding...

example of a one-pass algorithm is the Sondik partially observable Markov decision process. Given any list as an input: Count the number of elements. Given...

using methods like Markov decision processes (MDPs) and dynamic programming. Puterman, Martin L. (1994). Markov decision processes: discrete stochastic dynamic...

played. The bandit problem is formally equivalent to a one-state Markov decision process. The regret ρ {\displaystyle \rho } after T {\displaystyle T} rounds...

reinforcement learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcements learning algorithms use dynamic programming...

State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning...

using decision theory, decision analysis, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks...

framework of Markov decision processes with incomplete information, what ultimately led to the notion of a Partially observable Markov decision process. In 1995...

University. He is noted for his work in Markov decision process, Gittins index, the multi-armed bandit, Markov chains and other related fields. Katehakis...

reinforcement learning if the environment is stochastic and a Markov decision process (MDP) is used. Research in learning automata can be traced back...

probability distribution (and the reward function) associated with the Markov decision process (MDP), which, in RL, represents the problem to be solved. The transition...

Adaptive Multi-stage Sampling (AMS) algorithm for the model of Markov decision processes. AMS was the first work to explore the idea of UCB-based exploration...

optimality condition in optimal control theory Markov decision process – Mathematical model for sequential decision making under uncertainty Optimal control...

the stage payoffs. Stochastic games generalize Markov decision processes to multiple interacting decision makers, as well as strategic-form games to dynamic...

time are represented by probability distributions describing a markov decision process and the cycle of perception and action treated as an information...

allows efficient description of Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs) by representing everything...

which is, for the example, E(3) = 3.5 slots. Control theory Markov chain Markov decision process Tanenbaum & Wetherall 2010, p. 395 Rosenberg et al. RFC3261...

multi-agent influence diagrams, in particular dependency graphs, and Markov decision processes to solve multiscale challenges in sociotechnical systems. MSDT...

For the Markov decision process, the main ideas is to transform the optimization into an infinite horizon average reward Markov decision process. For a...

American scholar of computer science whose research has focused on Markov decision processes, queuing theory, computer networks, peer-to-peer networks, Internet...