Robust Markov decision processes (MDPs) provide a general framework to model decision
problems where the system dynamics are changing or only partially known. Efficient …

Safety is a critical factor in reinforcement learning (RL) in chemical processes. In our
previous work, we had proposed a new stability‐guaranteed RL for unconstrained nonlinear …

Transferring knowledge among various environments is important for efficiently learning
multiple tasks online. Most existing methods directly use the previously learned models or …

We study reinforcement learning (RL) for decision processes with non-Markovian reward, in
which high-level knowledge in the form of reward machines is available to the learner …

Recent success stories in reinforcement learning have demonstrated that leveraging
structural properties of the underlying environment is key in devising viable methods …

Animals are able to rapidly infer from limited experience when sets of state action pairs have
equivalent reward and transition dynamics. On the other hand, modern reinforcement …

We present an efficient robust value iteration for\texttt {s}-rectangular robust Markov Decision
Processes (MDPs) with a time complexity comparable to standard (non-robust) MDPs which …

We consider the situation when a learner faces a set of unknown discrete distributions
$(p_k) _ {k\in\mathcal K} $ defined over a common alphabet $\mathcal X $, and can build for …

We focus on s-rectangular robust Markov decision processes (MDPs), which capture
interconnected uncertainties across different actions within each state. This framework is …

The past decade has witnessed a rapid development of reinforcement learning (RL)
techniques. However, there is still a gap between employing RL in simulators and applying …