The framework of the Configurable Markov Decision Processes (Conf-MDPs, Metelli et al., 2018, 2019, 2022) has been introduced in recent years to model a wide range of real-world scenarios in which an agent has the opportunity to alter some environmental parameters in order to improve its learning experience. Conf-MDPs can be thought to as an extension of the traditional Markov Decision Processes (MDP, Puterman, 1994) to account for scenarios that emerge quite often in the Reinforcement Learning (RL, Sutton and Barto, 2018) problems, in which the environment rarely represents an immutable entity and can, indeed, be subject to partial control. In the Conf-MDP framework, the activity of altering the environmental parameters is named environment configuration and serves different purposes. In the simplest scenario, the configuration is carried out by the agent itself that acts as a configurator. This might suggest, at a first sight, that environment configuration can be modeled within the agent actuation.

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 trade imbalance leads to Empty Container Repositioning (ECR) problems. Once the freight has been delivered from an exporting country to an importing one, the laden will turn into empty containers that need to be repositioned to satisfy new goods requests in exporting countries. In such problems, the performance that any cooperative repositioning policy can achieve strictly depends on the routes that vessels will follow (i.e., fleet deployment). Historically, Operation Research (OR) approaches were proposed to jointly optimize the repositioning policy along with the fleet of vessels. However, the stochasticity of future supply and demand of containers, together with black-box and non-linear constraints that are present within the environment, make these approaches unsuitable for these scenarios. In this paper, we introduce a novel framework, Configurable Semi-POMDPs, to model this type of problems. Furthermore, we provide a two-stage learning algorithm, "Configure & Conquer" (CC), that first configures the environment by finding an approximation of the optimal fleet deployment strategy, and then "conquers" it by learning an ECR policy in this tuned environmental setting. We validate our approach in large and real-world instances of the problem. Our experiments highlight that CC avoids the pitfalls of OR methods and that it is successful at optimizing both the ECR policy and the fleet of vessels, leading to superior performance in world trade environments.

In many real-world problems, there is the possibility to configure, to a limited extent, some environmental parameters to improve the performance of a learning agent. In this paper, we propose a novel framework, Configurable Markov Decision Processes (Conf-MDPs), to model this new type of interaction with the environment. Furthermore, we provide a new learning algorithm, Safe Policy-Model Iteration (SPMI), to jointly and adaptively optimize the policy and the environment configuration. After having introduced our approach and derived some theoretical results, we present the experimental evaluation in two explicative problems to show the benefits of the environment configurability on the performance of the learned policy.