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 …

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 …

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 …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Cooperative multi-robot tasks can benefit from heterogeneity in the robots' physical and
behavioral traits. In spite of this, traditional Multi-Agent Reinforcement Learning (MARL) …

The necessity for cooperation among intelligent machines has popularised cooperative multi-
agent reinforcement learning (MARL) in AI research. However, many research endeavours …

Utilizing messages from teammates can improve coordination in cooperative Multi-agent
Reinforcement Learning (MARL). To obtain meaningful information for decision-making …

Bayesian inference has many advantages in robotic motion planning over four perspectives:
The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees of …

Cooperative multi-agent reinforcement learning (MARL) is making rapid progress for solving
tasks in a grid world and real-world scenarios, in which agents are given different attributes …

Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in
recent years. For training efficiency and scalability, most of the MARL algorithms make all …