In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applications
has progressively increased thanks to advancements in multi-agent system technology …

Recent studies in reinforcement learning (RL) have made significant progress by leveraging
function approximation to alleviate the sample complexity hurdle for better performance …

We present a novel actor-critic algorithm for an environment with delayed feedback, which
addresses the state-space explosion problem of conventional approaches. Conventional …

In real-world reinforcement learning (RL) systems, various forms of {\it impaired
observability} can complicate matters. These situations arise when an agent is unable to …

Many real-world offline reinforcement learning (RL) problems involve continuous-time
environments with delays. Such environments are characterized by two distinctive features …

Realtime environments change even as agents perform action inference and learning, thus
requiring high interaction frequencies to effectively minimize long-term regret. However …

Advances in the field of reinforcement learning (RL)-based drive control allow formulation of
holistic optimization goals for the data-driven training phase. The resulting controllers …

Several real-world scenarios, such as remote control and sensing, are comprised of action
and observation delays. The presence of delays degrades the performance of reinforcement …

When the agent's observations or interactions are delayed, classic reinforcement learning
tools usually fail. In this paper, we propose a simple yet new and efficient solution to this …

Vehicle-to-everything (V2X) technology is pivotal for enhancing road safety, traffic efficiency,
and energy conservation through the communication of vehicles with their surrounding …