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

Reinforcement learning is challenging in delayed scenarios, a common real-world situation
where observations and interactions occur with delays. State-of-the-art (SOTA) state …

Delays are inherent to most dynamical systems. Besides shifting the process in time, they
can significantly affect their performance. For this reason, it is usually valuable to study the …

In reinforcement learning, the Markov Decision Process (MDP) framework typically operates
under a blocking paradigm, assuming a static environment during the agent's decision …

Abstract Cybertwin-enabled 6th Generation (6G) network is envisioned to support artificial
intelligence-native management to meet changing demands of 6G applications. Multi-Agent …

In environments with delayed observation, state augmentation by including actions within
the delay window is adopted to retrieve Markovian property to enable reinforcement learning …

Local-remote systems allow robots to execute complex tasks in hazardous environments
such as space and nuclear power stations. However, establishing accurate positional …

Reinforcement learning is one of the most general paradigms to solve sequential decision
making issues on the assumption that the action selection and environmental feedback are …

Delays in real-world tasks degrade the performance of standard reinforcement learning (RL)
which is based on the assumption that environmental feedback and action selection are …

Reinforcement learning (RL) is challenging in the common case of delays between events
and their sensory perceptions. State-of-the-art (SOTA) state augmentation techniques either …