Results 51 to 60 of about 56,185 (161)

A Novel Model for Optimizing Roundabout Merging Decisions Based on Markov Decision Process and Force-Based Reward Function

Mathematics
Autonomous vehicles (AVs) are increasingly operating in complex traffic environments where safe and efficient decision-making is crucial. Merging into roundabouts is a key interaction scenario.
Qingyuan Shen, Haobin Jiang, Aoxue Li, Shidian Ma   +3 more
doaj   +1 more source

Safe Reinforcement Learning for Arm Manipulation with Constrained Markov Decision Process

Robotics
In the world of human–robot coexistence, ensuring safe interactions is crucial. Traditional logic-based methods often lack the intuition required for robots, particularly in complex environments where these methods fail to account for all possible ...
Patrick Adjei, Norman Tasfi, Santiago Gomez-Rosero, Miriam A. M. Capretz   +3 more
doaj   +1 more source

Open-Ended Learning: A Conceptual Framework Based on Representational Redescription

Frontiers in Neurorobotics, 2018
Reinforcement learning (RL) aims at building a policy that maximizes a task-related reward within a given domain. When the domain is known, i.e., when its states, actions and reward are defined, Markov Decision Processes (MDPs) provide a convenient ...
Stephane Doncieux, David Filliat, Natalia Díaz-Rodríguez, Timothy Hospedales, Richard Duro, Alexandre Coninx, Diederik M. Roijers, Benoît Girard, Nicolas Perrin, Olivier Sigaud   +9 more
doaj   +1 more source

Extending the Logic IM-SPDL with Impulse and State Rewards [PDF]

, 2007
This report presents the logic SDRL (Stochastic Dynamic Reward Logic), an extension of the stochastic logic IM-SPDL, which supports the specication of complex performance and dependability requirements.
Haverkort, Boudewijn R., Kuntz, Matthias
core   +1 more source

CAMP: Counterexamples, Abstraction, MDPs, and Policy Refinement for Enhancing Safety, Stability, and Rewards in Reinforcement Learning

IEEE Access
Reinforcement learning (RL) has demonstrated exceptional performance across various real-world applications such as autonomous driving, robotic control, and finance.
Ryeonggu Kwon, Gihwon Kwon
doaj   +1 more source

Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning [PDF]

, 2012
Many problems in sequential decision making and stochastic control often have natural multiscale structure: sub-tasks are assembled together to accomplish complex goals.
Bouvrie, Jake, Maggioni, Mauro
core  

Multi-Agent Deep Reinforcement Learning for Anti-Aircraft Artillery Systems in Counter-UAV Operations [PDF]

Hangkong bingqi
To address the issues of low engagement efficiency and insufficient adaptability in current coun-ter unmanned aerial vehicle (UAV) artillery systems, this paper proposes a situational-fused hierarchical multi-objective multi-agent reinforcement learning ...
Hu Jiawei, Dai Changhua, Qi Wanlong, Chen Zhiheng, Wang Zhen, Fan Bohao, Zheng Xinlei, Tang Jie
doaj   +1 more source

Planning Under Uncertainty Applications in Power Plants Using Factored Markov Decision Processes

Energies, 2020
Due to its ability to deal with non-determinism and partial observability, represent goals as an immediate reward function and find optimal solutions, planning under uncertainty using factored Markov Decision Processes (FMDPs) has increased its ...
Alberto Reyes, L. Enrique Sucar, Pablo H. Ibargüengoytia, Eduardo F. Morales   +3 more
doaj   +1 more source

Maximizing the probability of attaining a target prior to extinction

, 2009
We present a dynamic programming-based solution to the problem of maximizing the probability of attaining a target set before hitting a cemetery set for a discrete-time Markov control process.
Abate, Aliprantis, Bertsekas, Bertsekas, Boda, Borkar, Borkar, Bouakiz, Chatterjee, Debasish Chatterjee, Derman, Digaĭlova, Dubins, Dynkin, Eaton, Eugenio Cinquemani, Gao, Hernández-Lerma, Hernández-Lerma, John Lygeros, Kesten, Kushner, Kwiatkowska, Levin, Meyn, Ohtsubo, Peskir, Powell, Prajna, Prandini, Ramponi, Rao, Schmidli, Simon, Summers, Tomlin, Whittle, Zhu   +37 more
core   +4 more sources

Mode Selection and Resource Allocation in Device-to-Device Communications With User Arrivals and Departures

IEEE Access, 2016
The pervasive increasing mobile devices and explosively increasing data traffic pose imminent challenges on wireless network design. Device-to-device (D2D) communication is envisioned to play a key role in the fifth generation cellular networks to ...
Lei Lei, Qingyun Hao, Zhangdui Zhong
doaj   +1 more source