The availability of challenging benchmarks has played a key role in the recent progress of
machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi …

Adaptive behavior often requires predicting future events. The theory of reinforcement
learning prescribes what kinds of predictive representations are useful and how to compute …

Multi-agent reinforcement learning (MARL) has witnessed significant progress with the
development of value function factorization methods. It allows optimizing a joint action-value …

Grou** is ubiquitous in natural systems and is essential for promoting efficiency in team
coordination. This paper proposes a novel formulation of Group-oriented Multi-Agent …

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 …

Abstract The Option Keyboard (OK) was recently proposed as a method for transferring
behavioral knowledge across tasks. OK transfers knowledge by adaptively combining …

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 …

Reinforcement Learning in large action spaces is a challenging problem. This is especially
true for cooperative multi-agent reinforcement learning (MARL), which often requires …

The difficulty of appropriately assigning credit is particularly heightened in cooperative
MARL with sparse reward, due to the concurrent time and structural scales involved …

Tackling overestimation in $ Q $-learning is an important problem that has been extensively
studied in single-agent reinforcement learning, but has received comparatively little attention …