The emergence of the Internet of things and the widespread deployment of diverse
computing systems have led to the formation of heterogeneous multi-agent systems (MAS) …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

Sequential decision-making problems with multiple objectives arise naturally in practice and
pose unique challenges for research in decision-theoretic planning and learning, which has …

Intelligent transport systems, efficient electric grids, and sensor networks for data collection
and analysis are some examples of the multiagent systems (MAS) that cooperate to achieve …

Evolutionary robotics applies the selection, variation, and heredity principles of natural
evolution to the design of robots with embodied intelligence. It can be considered as a …

A variety of Reinforcement Learning (RL) techniques blends with one or more techniques
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …

Reinforcement learning is a major tool to realize intelligent agents that can be autonomously
adaptive to the environment. With deep models, reinforcement learning has shown great …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

This paper addresses the general problem of reinforcement learning (RL) in partially
observable environments. In 2013, our large RL recurrent neural networks (RNNs) learned …

Many real-world decision problems have multiple objectives. For example, when choosing a
medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize …