Reinforcement Learning for Process Control: Review and Benchmark Problems
J Park, H Jung, JW Kim, JM Lee - International Journal of Control …, 2025 - Springer
The success of reinforcement learning (RL) combined with deep neural networks has led to
the development of numerous RL algorithms that have demonstrated remarkable …
the development of numerous RL algorithms that have demonstrated remarkable …
[HTML][HTML] Multi-agent active multi-target search with intermittent measurements
Consider a multi-agent system that must find an unknown number of static targets at
unknown locations as quickly as possible. To estimate the number and positions of targets …
unknown locations as quickly as possible. To estimate the number and positions of targets …
A collective approach to reach known and unknown target in multi agent environment using nature inspired algorithms
Robotics is a vast and growing area in academia and industry, and it is used to solve human
problems through artificial intelligent machines. To solve crucial issues or problems such as …
problems through artificial intelligent machines. To solve crucial issues or problems such as …
Kalman Filter-Based Distributed Gaussian Process for Unknown Scalar Field Estimation in Wireless Sensor Networks
In this letter, we propose an online scalar field estimation algorithm of unknown
environments using a distributed Gaussian process (DGP) framework in wireless sensor …
environments using a distributed Gaussian process (DGP) framework in wireless sensor …
Robust Online Multi-robot Simultaneous Exploration and Coverage Path Planning.
In this paper, a robust online Multi-robot Simultaneous exploration and coverage path
planning problem is presented. The entire workspace is initially partitioned using a variant of …
planning problem is presented. The entire workspace is initially partitioned using a variant of …