markov decision processes

https://deepai.org/publication/an-approximate-solution-method-for-large-risk-averse-markov-decision-processes An Approximate Solution Method for Large Risk-Averse Markov Decision Processes | DeepAI Oct 16, 2012 - 10/16/12 - Stochastic domains often involve risk-averse decision makers. While recent work has focused on how to model risk in Markov decisio... markov decision processes https://deepai.org/publication/partially-observable-markov-decision-processes-pomdps-and-robotics Partially Observable Markov Decision Processes (POMDPs) and Robotics | DeepAI Jul 15, 2021 - 07/15/21 - Planning under uncertainty is critical to robotics. The Partially Observable Markov Decision Process (POMDP) is a mathematical fra... markov decision processes partially observable robotics deepai https://deepai.org/publication/horizon-free-reinforcement-learning-for-latent-markov-decision-processes Horizon-Free Reinforcement Learning for Latent Markov Decision Processes | DeepAI Oct 20, 2022 - 10/20/22 - We study regret minimization for reinforcement learning (RL) in Latent Markov Decision Processes (LMDPs) with context in hindsight... markov decision processes reinforcement learning horizon free latent https://openreview.net/forum?id=H4Fb5RZQgr Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes | OpenReview We study reinforcement learning (RL) with linear function approximation. For episodic time-inhomogeneous linear Markov decision processes (linear MDPs) whose... markov decision processes reinforcement learning nearly minimax optimal https://openreview.net/forum?id=gdVcFOvxT3 Finding Safe Zones of Markov Decision Processes Policies | OpenReview Given a policy of a Markov Decision Process, we define a SafeZone as a subset of states, such that most of the policy's trajectories are confined to this... markov decision processes safe zones finding policies openreview https://openreview.net/forum?id=GdsbEOwAE7&referrer=%5Bthe%20profile%20of%20Matthew%20E.%20Taylor%5D(%2Fprofile%3Fid%3D~Matthew_E._Taylor2) Model-Based Exploration in Monitored Markov Decision Processes | OpenReview A tenet of reinforcement learning is that the agent always observes rewards. However, this is not true in many realistic settings, e.g., a human observer may... markov decision processes model based exploration monitored https://openreview.net/forum?id=VJwsDwuiuH Rate-Optimal Policy Optimization for Linear Markov Decision Processes | OpenReview We study regret minimization in online episodic linear Markov Decision Processes, and propose a policy optimization algorithm that is computationally... markov decision processes rate optimal policy optimization https://arxiv.org/abs/2407.00388 [2407.00388] Weighted mesh algorithms for general Markov decision processes: Convergence and... Abstract page for arXiv paper 2407.00388: Weighted mesh algorithms for general Markov decision processes: Convergence and tractability markov decision processes https://openreview.net/forum?id=6lP80vBiI6 Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes | OpenReview We study semiparametrically efficient estimation in off-policy evaluation (OPE) where the underlying Markov decision process (MDP) is linear with a known... markov decision processes policy evaluation efficient https://openreview.net/forum?id=rF-eW_Lsqgc Optimal Control of Partially Observable Markov Decision Processes with Finite Linear Temporal Logic... Reward optimal control of POMDPs with Linear Temporal Logic constraints markov decision processes https://deepai.org/publication/robust-anytime-learning-of-markov-decision-processes Robust Anytime Learning of Markov Decision Processes | DeepAI May 31, 2022 - 05/31/22 - Markov decision processes (MDPs) are formal models commonly used in sequential decision-making. MDPs capture the stochasticity tha... markov decision processes robust anytime learning deepai https://deepai.org/publication/model-free-reinforcement-learning-for-branching-markov-decision-processes Model-free Reinforcement Learning for Branching Markov Decision Processes | DeepAI Jun 12, 2021 - 06/12/21 - We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (mu... markov decision processes model free reinforcement learning branching deepai https://openreview.net/forum?id=02r24A8doi&referrer=%5Bthe%20profile%20of%20Zhiyuan%20Fan%5D(%2Fprofile%3Fid%3D~Zhiyuan_Fan1) Achieving Constant Regret in Linear Markov Decision Processes | OpenReview We study the constant regret guarantees in reinforcement learning (RL). Our objective is to design an algorithm that incurs only finite regret over infinite... markov decision processes achieving constant regret linear https://openreview.net/forum?id=cm53OBkctM Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite... Models of many real-life applications, such as queueing models of communication networks or computing systems, have a countably infinite state-space.... markov decision processes bayesian learning https://openreview.net/forum?id=PRu8Sybp1j&referrer=%5Bthe%20profile%20of%20Emil%20Javurek%5D(%2Fprofile%3Fid%3D~Emil_Javurek1) An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes | OpenReview Predicting individualized potential outcomes in sequential decision-making is central for optimizing therapeutic decisions in personalized medicine (e.g.,... markov decision processes orthogonal learner individualized https://www.muni.cz/en/research/publications/1674916 Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes | Masaryk... markov decision processes reinforcement learning risk constrained https://deepai.org/publication/policy-iteration-for-decentralized-control-of-markov-decision-processes Policy Iteration for Decentralized Control of Markov Decision Processes | DeepAI Jan 15, 2014 - 01/15/14 - Coordination of distributed agents is required for problems arising in many areas, including multi-robot systems, networking and e... markov decision processes policy iteration decentralized control deepai https://www.free-ebooks.net/robotics-academic/Global-Navigation-of-Assistant-Robots-using-Partially-Observable-Markov-Decision-Processes Global Navigation of Assistant Robots using Partially Observable Markov Decision Processes, by... Free download of Global Navigation of Assistant Robots using Partially Observable Markov Decision Processes by Maria Elena Lopez, Rafael Barea, Luis Miguel... markov decision processes https://slides.com/shensquared/introml-sp26-lec10 6.390 IntroML (Spring 26) - Lecture 10 Markov Decision Processes A presentation created with Slides. spring 26 lecture 10 390 markov decision https://deepai.org/publication/stateful-posted-pricing-with-vanishing-regret-via-dynamic-deterministic-markov-decision-processes Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes |... May 4, 2020 - 05/04/20 - In this paper, a rather general online problem called dynamic resource allocation with capacity constraints (DRACC) is introduced ... https://openreview.net/forum?id=Gf15GsnfTy REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes |... Discrete-action reinforcement learning algorithms often falter in tasks with high-dimensional discrete action spaces due to the vast number of possible... ensemble value decomposition markov decision https://arxiv.org/abs/1802.09810 [1802.09810] Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes Abstract page for arXiv paper 1802.09810: Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes human in the loop https://arxiv.org/abs/2510.20725 [2510.20725] No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian... Abstract page for arXiv paper 2510.20725: No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes https://arxiv.org/abs/1906.10640 [1906.10640] SOS: Safe, Optimal and Small Strategies for Hybrid Markov Decision Processes Abstract page for arXiv paper 1906.10640: SOS: Safe, Optimal and Small Strategies for Hybrid Markov Decision Processes https://arxiv.org/abs/2201.11206v1 [2201.11206v1] Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes Abstract page for arXiv paper 2201.11206v1: Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes

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