The control of continuous-time environments while actively deciding when to take costly
observations in time is a crucial yet unexplored problem, particularly relevant to real-world …

Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this
work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels …

We study Markov decision processes (MDPs), where agents control when and how they
gather information, as formalized by action-contingent noiselessly observable MDPs (ACNO …

Reinforcement learning (RL) has been shown to learn sophisticated control policies for
complex tasks including games, robotics, heating and cooling systems and text generation …

In Cyber Physical Systems (CPSs), two groups of actors interact toward the maximization of
system performance: the sensors, observing and disseminating the system state, and the …

Deep reinforcement learning (DRL) techniques have become increasingly used in various
fields for decision-making processes. However, a challenge that often arises is the trade-off …

In this paper, we study the remote estimation problem of a Markov process over a channel
with a cost. We formulate this problem as an infinite horizon optimization problem with two …

Effective management of multi-intersection traffic signal control (MTSC) is vital for intelligent
transportation systems. Multi-agent reinforcement learning (MARL) has shown promise in …

Objective. The data processing of medical test report has always been one of the important
contents in biological information domain, especially the process of extracting the effective …

Pragmatic or goal-oriented communication can optimize communication decisions beyond
the reliable transmission of data, instead aiming at directly affecting application performance …