Application of reinforcement learning and deep learning in multiple-input and multiple-output (MIMO) systems
The current wireless communication infrastructure has to face exponential development in
mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems …
mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems …
Inverse reinforcement learning as the algorithmic basis for theory of mind: current methods and open problems
J Ruiz-Serra, MS Harré - Algorithms, 2023 - mdpi.com
Theory of mind (ToM) is the psychological construct by which we model another's internal
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …
Learning and assessing optimal dynamic treatment regimes through cooperative imitation learning
Dynamic Treatment Regimes (DTRs) are sets of sequential decision rules that can be
adapted over time to treat patients with a specific pathology. DTR consists of alternative …
adapted over time to treat patients with a specific pathology. DTR consists of alternative …
Recognition and interfere deceptive behavior based on inverse reinforcement learning and game theory
Y Zeng, K Xu - Journal of Systems Engineering and Electronics, 2023 - ieeexplore.ieee.org
In real-time strategy (RTS) games, the ability of recognizing other players' goals is important
for creating artifical intelligence (AI) players. However, most current goal recognition …
for creating artifical intelligence (AI) players. However, most current goal recognition …
Machine Learning Based Mechanical Fault Diagnosis and Detection Methods: A Systematic Review
Y Xin, J Zhu, M Cai, P Zhao, Q Zuo - Measurement Science and …, 2024 - iopscience.iop.org
Mechanical fault diagnosis and detection are crucial for enhancing equipment reliability,
economic efficiency, production safety, and energy conservation. In the era of Industry 4.0 …
economic efficiency, production safety, and energy conservation. In the era of Industry 4.0 …
Hybrid model process design of joint operator-robot interaction within a synergistic system
M Gorkavyy, Y Ivanov, D Grabar… - AIP Conference …, 2023 - pubs.aip.org
The paper deals with the issues of increasing the efficiency of functioning of synergetic
robotic systems by searching for waysto improve the simulationmodels underlying them …
robotic systems by searching for waysto improve the simulationmodels underlying them …
Real-time planning of Optimal Route for Conflict-Free UAS Operation Using Deep Reinforcement Learning
View Video Presentation: https://doi. org/10.2514/6.2023-2659. vid Unmanned aircraft
systems (UAS) have now been standard and practical in many applications due to their …
systems (UAS) have now been standard and practical in many applications due to their …
[PDF][PDF] PERSONALIZED DRIVING USING INVERSE REINFORCEMENT
RJG SALINAS - 2024 - utrgv.edu
ABSTRACT Gonzalez Salinas, Rodrigo J., Personalized Driving using Inverse
Reinforcement Learning. Master of Science in Engineering (MSE), May, 2024,## pp.,# …
Reinforcement Learning. Master of Science in Engineering (MSE), May, 2024,## pp.,# …
Personalized Driving using Inverse Reinforcement Learning
RJ Gonzalez - 2024 - scholarworks.utrgv.edu
This thesis introduces an autonomous driving controller designed to replicate individual
driving behaviors based on a provided demonstration. The controller employs Inverse …
driving behaviors based on a provided demonstration. The controller employs Inverse …
Guided Cost Learning for Lunar Lander Environment Using Human Demonstrated Expert Trajectories
Inverse Reinforcement Learning is a subset of Imitation learning, where the goal is to
generate a reward function that captures an expert's behavior using a set of demonstrations …
generate a reward function that captures an expert's behavior using a set of demonstrations …