  It can largely benefit the reinforcement learning (RL) process of each agent
if multiple geographically distributed agents perform their separate RL tasks
cooperatively. Different from multi-agent reinforcement learning (MARL) where
multiple agents are in a common environment and should learn to cooperate or
compete with each other, in this case each agent has its separate environment
and only communicates with others to share knowledge without any cooperative or
competitive behaviour as a learning outcome. In fact, this scenario exists
widely in real life whose concept can be utilised in many applications, but is
not well understood yet and not well formulated. As the first effort, we
propose group-agent system for RL as a formulation of this scenario and the
third type of RL system with respect to single-agent and multi-agent systems.
We then propose a distributed RL framework called DDAL (Decentralised
Distributed Asynchronous Learning) designed for group-agent reinforcement
learning (GARL). We show through experiments that DDAL achieved desirable
performance with very stable training and has good scalability.
