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) …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

Many real-world tasks involve multiple agents with partial observability and limited
communication. Learning is challenging in these settings due to local viewpoints of agents …

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …

Collective human knowledge has clearly benefited from the fact that innovations by
individuals are taught to others through communication. Similar to human social groups …

Some researchers have introduced transfer learning mechanisms to multiagent
reinforcement learning (MARL). However, the existing works devoted to cross-task transfer …

Domestic hot water accounts for approximately 15% of the total residential energy
consumption in Europe, and most of this usage happens during specific periods of the day …

In this paper, we present an evolutionary transfer reinforcement learning framework (eTL) for
developing intelligent agents capable of adapting to the dynamic environment of multiagent …

Multiagent reinforcement learning (MARL) algorithms have been demonstrated on complex
tasks that require the coordination of a team of multiple agents to complete. Existing works …

Large-scale, multi-agent systems are too complex for optimal control strategies to be known
at design time and as a result good strategies must be learned at runtime. Learning in such …