The choice of the control frequency of a system has a relevant impact on the ability of
reinforcement learning algorithms to learn a highly performing policy. In this paper, we …

Reinforcement Learning (RL) has emerged as an effective approach to address a variety of
complex control tasks. In a typical RL problem, an agent interacts with the environment by …

Abstract The Configurable Markov Decision Process framework includes two entities: a
Reinforcement Learning agent and a configurator that can modify some environmental …

In this paper, we investigate the optimal online configuration of episodic Markov decision
processes when the space of the possible configurations is continuous. Specifically, we …

We study the problem of identifying the policy space available to an agent in a learning
process, having access to a set of demonstrations generated by the agent playing the …

In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to
address complex control tasks. In a Markov Decision Process (MDP), the framework typically …

With the continuous growth of the global economy and markets, resource imbalance has
risen to be one of the central issues in real logistic scenarios. In marine transportation, this …

Configurable Markov Decision Processes (Conf-MDPs) have recently been introduced as an
extension of the traditional Markov Decision Processes (MDPs) to model the real-world …

Reinforcement Learning (RL) has emerged as the standard paradigm for sequential
decision-making and a framework for general intelligence. At its core, the RL problem is one …

In this paper, we provide a unified presentation of the Configurable Markov Decision
Process (Conf-MDP) framework. A Conf-MDP is an extension of the traditional Markov …