This article discusses some trends and concepts in developing a new generation of future
Artificial General Intelligence (AGI) systems which relate to complex facets and different …

Reinforcement learning has been used to solve sequence decision problems without
models for decades. However, it often faces great challenges in dealing with high …

Deep reinforcement learning algorithms have recently been used to train multiple interacting
agents in a centralised manner whilst keeping their execution decentralised. When the …

Agents that interact with other agents often do not know a priori what the other agents'
strategies are, but have to maximise their own online return while interacting with and …

In cooperative multi-agent reinforcement learning, a collection of agents learns to interact in
a shared environment to achieve a common goal. We propose the use of reward machines …

Connected and automated vehicles (CAVs) require multiple tasks in their seamless
maneuverings. Some essential tasks that require simultaneous management and actions …

This paper addresses the problem of collaborative multi-agent autonomous driving of
connected and automated vehicles (CAVs) in lane-free highway scenarios. We eliminate the …

This survey provides a comprehensive analysis on recent research related to optimization
and simulation in the new paradigm of power systems, which embraces the so‐called smart …

The main challenge of multiagent reinforcement learning is the difficulty of learning useful
policies in the presence of other simultaneously learning agents whose changing behaviors …

In a one‐on‐one air combat game, the opponent's maneuver strategy is usually not
deterministic, which leads us to consider a variety of opponent's strategies when designing …