Results 111 to 120 of about 154,449 (307)
Risk-Sensitive Reinforcement Learning: A Constrained Optimization Viewpoint
, 2018 The classic objective in a reinforcement learning (RL) problem is to find a policy that minimizes, in expectation, a long-run objective such as the infinite-horizon discounted or long-run average cost.
A., Prashanth L., Fu, Michael
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Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai +3 more
wiley +1 more source
Bounding Rationality by Discounting Time [PDF]
, 2009 Consider a game where Alice generates an integer and Bob wins if he can factor that integer. Traditional game theory tells us that Bob will always win this game even though in practice Alice will win given our usual assumptions about the hardness of ...
Fortnow, Lance, Santhanam, Rahul
core +4 more sources
Advanced Intelligent Systems, EarlyView.This study presents a multitask strategy for plastic cleanup with autonomous surface vehicles, combining exploration and cleaning phases. A two‐headed Deep Q‐Network shared by all agents is traineded via multiobjective reinforcement learning, producing a Pareto front of trade‐offs.
Dame Seck +4 more
wiley +1 more source
Public infrastructures, public consumption and welfare in a new open economy macro model [PDF]
This paper focuses on the trade-off faced by governments in deciding the allocation of public expenditures between productivity-enhancing public infrastructures and utility-enhancing public consumption in a two-country model.
Ganelli , Giovanni, Tervala, Juha
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Discounting and Future Selves [PDF]
, 2002 Is discounting of future instantaneous utilities consistent with altruism towards future selves? More precisely, can temporal preferences, expressed as a sum of discounted instantaneous utilities, be derived from a representation in the form of a sum of ...
Sáez-Martí, María +1 more
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Advanced Intelligent Systems, EarlyView.This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi +3 more
wiley +1 more source
Characterization of a Cournot–Nash Equilibrium for a Fishery Model with Fuzzy Utilities
Journal of MathematicsThe article deals with the extensions of discrete-time games with infinite time horizon and their application in a fuzzy context to fishery models. The criteria for these games are the total discounted utility and the average utility in a fishing problem.
R. Israel Ortega-Gutiérrez +2 more
doaj +1 more source
Money Metrics Welfare Measures in Imperfect Markets under Growth [PDF]
This paper shows how utility based welfare measures in dynamic general equilibrium under imperfect markets can be transferred into a money metrics. In order to do this, we need to price forward looking components measured in units of utility. The typical
Li, Chuan Zhong, Löfgren, Karl-Gustaf
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Collaborative Multiagent Closed‐Loop Motion Planning for Multimanipulator Systems
Advanced Intelligent Systems, EarlyView.This work presents a hierarchical multi‐manipulator planner, emphasizing highly overlapping space. The proposed method leverages an enhanced Dynamic Movement Primitive based planner along with an improvised Multi‐Agent Reinforcement Learning approach to ensure regulatory and mediatory control while ensuring low‐level autonomy. Experiments across varied
Tian Xu, Siddharth Singh, Qing Chang
wiley +1 more source