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 …

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 …

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

Classic reinforcement learning (RL) frequently struggles with tasks involving delays due to
the violation of the Markov property. Existing approaches usually tackle this issue with end …

Current advancements in reinforcement learning (RL) have predominantly focused on
learning step-based policies that generate actions for each perceived state. While these …

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 …

Observation and action delays occur commonly in many real-world tasks which violate
Markov property and consequently degrade the performance of Reinforcement Learning …

Offline reinforcement learning aims to utilize datasets of previously gathered environment-
action interaction records to learn a policy without access to the real environment. Recent …

Deep Reinforcement Learning (DRL) has shown outstanding performance on inducing
effective action policies that maximize expected long-term return on many complex tasks …

Deep reinforcement learning (DRL) is advantageous, but it rarely performs well when tested
on real-world decision-making tasks, particularly those involving irregular time series with …