Abstract Purpose of Review Current real-world interaction between humans and robots is
extremely limited. We present challenges that, if addressed, will enable humans and robots …

Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …

Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve
complex cooperative tasks. Its success is partly because of parameter sharing among …

Exploration is critical for good results in deep reinforcement learning and has attracted much
attention. However, existing multi-agent deep reinforcement learning algorithms still use …

Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used
evaluation tasks and criteria, making comparisons between approaches difficult. In this work …

Sharing parameters in multi-agent deep reinforcement learning has played an essential role
in allowing algorithms to scale to a large number of agents. Parameter sharing between …

Multi-agent reinforcement learning (MARL) has emerged as a useful approach to solving
decentralised decision-making problems at scale. Research in the field has been growing …

Metaverse has gained increasing interest in education, with much of literature focusing on its
great potential to enhance both individual and social aspects of learning. However, little …

In this paper, we consider cooperative multi-agent reinforcement learning (MARL) with
sparse reward. To tackle this problem, we propose a novel method named MASER: MARL …

Many advances in cooperative multi-agent reinforcement learning (MARL) are based on two
common design principles: value decomposition and parameter sharing. A typical MARL …