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Many real-life problems involve dealing with multiple objectives. For example, in network routing the criteria may consist of energy consumption, latency, and channel capacity, which are in essence conflicting objectives. As in many problems there may be multiple (conflicting) objectives, there usually does not exist a single optimal solution. In those cases, it is desirable to obtain a set of trade-off solutions between the objectives. This problem has in the last decade also gained the attention of many researchers in the field of reinforcement learning (RL). RL addresses sequential decision problems in initially (possibly) unknown stochastic environments. The goal is the maximization of the agent's reward in an environment that is not always completely observable. The purpose of this special issue is to obtain a broader picture on the algorithmic techniques at the confluence between multi-objective optimization and reinforcement learning. The growing interest in multi-objective reinforcement learning (MORL) was reflected in the quantity and quality of submissions received for this special issue. After a rigorous review process, seven papers were accepted for publication, and they reflect the diversity of research being carried out within this emerging field of research. The accepted papers consider many different aspects of algorithmic design and the evaluation and this editorial puts them in a unified framework.

Mathematical models with varying degrees of complexity have been proposed and simulated in an attempt to represent the intricate mechanisms of the human neuron. One of the most biochemically realistic and analytical models, based on the Hodgkin–Huxley (HH) model, has been selected for study in this paper. In order to satisfy the model’s computational demands, we present a simulator implemented on Intel Xeon Phi Knights Landing manycore processors. This high-performance platform features an x86-based architecture, allowing our implementation to be portable to other common manycore processing machines. This is reinforced by the fact that Phi adopts the popular OpenMP and MPI programming models. The simulator performance is evaluated when calculating neuronal networks of varying sizes, density and network connectivity maps. The evaluation leads to an analysis of the neuronal synaptic patterns and their impact on performance when tackling this type of workload on a multinode system. It will be shown that the simulator can calculate 100 ms of simulated brain activity for up to 2 millions of biophysically-accurate neurons and 2 billion neuronal synapses within one minute of execution time. This level of performance renders the application an efficient solution for large-scale detailed model simulation.