A data-driven approximate dynamic programming approach based on association rule learning: Spacecraft autonomy as a case study
Dynamic programming (DP) and Markov Decision Process (MDP) offer powerful tools for
formulating, modeling, and solving decision making problems under uncertainty. In real …
formulating, modeling, and solving decision making problems under uncertainty. In real …
Spacecraft autonomy modeled via Markov decision process and associative rule-based machine learning
Spacecraft on-board autonomy is an important topic in currently developed and future space
missions. In this study, we present a robust approach to the optimal policy of autonomous …
missions. In this study, we present a robust approach to the optimal policy of autonomous …
[HTML][HTML] A review of approximate dynamic programming applications within military operations research
M Rempel, J Cai - Operations Research Perspectives, 2021 - Elsevier
Sequences of decisions that occur under uncertainty arise in a variety of settings, including
transportation, communication networks, finance, defence, etc. The classic approach to find …
transportation, communication networks, finance, defence, etc. The classic approach to find …
Data mining for state space orthogonalization in adaptive dynamic programming
Dynamic programming (DP) is a mathematical programming approach for optimizing a
system that changes over time and is a common approach for developing intelligent …
system that changes over time and is a common approach for developing intelligent …
Efficient solutions to factored MDPs with imprecise transition probabilities
When modeling real-world decision-theoretic planning problems in the Markov Decision
Process (MDP) framework, it is often impossible to obtain a completely accurate estimate of …
Process (MDP) framework, it is often impossible to obtain a completely accurate estimate of …
State aggregation approximate dynamic programming for model-based spacecraft autonomy
Spacecraft autonomy is a crucial aspect of currently developed and future space projects.
This paper presents a Markovian Decision Process (MDP) based framework as a way of …
This paper presents a Markovian Decision Process (MDP) based framework as a way of …
Probabilistic differential dynamic programming
Y Pan, E Theodorou - Advances in Neural Information …, 2014 - proceedings.neurips.cc
We present a data-driven, probabilistic trajectory optimization framework for systems with
unknown dynamics, called Probabilistic Differential Dynamic Programming (PDDP). PDDP …
unknown dynamics, called Probabilistic Differential Dynamic Programming (PDDP). PDDP …
APRICODD: Approximate policy construction using decision diagrams
R St-Aubin, J Hoey, C Boutilier - Advances in Neural …, 2000 - proceedings.neurips.cc
We propose a method of approximate dynamic programming for Markov decision processes
(MDPs) using algebraic decision diagrams (ADDs). We produce near-optimal value …
(MDPs) using algebraic decision diagrams (ADDs). We produce near-optimal value …
Stochastic dynamic programming with factored representations
Markov decision processes (MDPs) have proven to be popular models for decision-theoretic
planning, but standard dynamic programming algorithms for solving MDPs rely on explicit …
planning, but standard dynamic programming algorithms for solving MDPs rely on explicit …
[HTML][HTML] Real-time dynamic programming for Markov decision processes with imprecise probabilities
Abstract Markov Decision Processes have become the standard model for probabilistic
planning. However, when applied to many practical problems, the estimates of transition …
planning. However, when applied to many practical problems, the estimates of transition …